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Introduction: Low socioeconomic status affects not only diagnosis rates and therapy of patients with diabetes mellitus but also their health behavior. Our primary goal was to examine diagnosis rates and therapy of individuals with diabetes living in Ormánság, one of the most deprived areas in Hungary and Europe. Our secondary goal was to examine the differences in lifestyle factors and cancer screening participation of patients with diagnosed and undiagnosed diabetes compared to healthy participants. Methods: Our study is a cross-sectional analysis using data from the "Ormánság Health Program". The "Ormánság Health Program" was launched to improve the health of individuals in a deprived region of Hungary. Participants in the program were coded as diagnosed diabetes based on diagnosis by a physician as a part of the program, self-reported diabetes status, and self-reported prescription of antidiabetic medication. Undiagnosed diabetes was defined as elevated blood glucose levels without self-reported diabetes and antidiabetic prescription. Diagnosis and therapeutic characteristics were presented descriptively. To examine lifestyle factors and screening participation, patients with diagnosed and undiagnosed diabetes were compared to healthy participants using linear regression or multinomial logistic regression models adjusted for sex and age. Results: Our study population consisted of 246 individuals, and 17.9% had either diagnosed (n=33) or undiagnosed (n=11) diabetes. Metformin was prescribed in 75.8% (n=25) of diagnosed cases and sodium-glucose cotransporter-2 inhibitors (SGLT-2) in 12.1% (n=4) of diagnosed patients. After adjustment, participants with diagnosed diabetes had more comorbidities (adjusted [aOR]: 3.50, 95% confidence interval [95% CI]: 1.34--9.18, p<0.05), consumed vegetables more often (aOR: 2.49, 95% CI: 1.07--5.78, p<0.05), but desserts less often (aOR: 0.33, 95% CI: 0.15--0.75, p<0.01) than healthy individuals. Patients with undiagnosed diabetes were not different in this regard from healthy participants. No significant differences were observed for cancer screening participation between groups. Conclusions: To increase recognition of diabetes, targeted screening tests should be implemented in deprived regions, even among individuals without any comorbidities. Our study also indicates that diagnosis of diabetes is not only important for the timely initiation of therapy, but it can also motivate individuals in deprived areas to lead a healthier lifestyle. [ABSTRACT FROM AUTHOR]

Diabetes is recognized as one of the most detrimental diseases worldwide, characterized by elevated levels of blood glucose stemming from either insulin deficiency or decreased insulin efficacy. Early diagnosis of diabetes enables patients to initiate treatment promptly, thereby minimizing or eliminating the risk of severe complications. Although years of research in computational diagnosis have demonstrated that machine learning offers a robust methodology for predicting diabetes, existing models leave considerable room for improvement in terms of accuracy. This paper proposes an improved ensemble machine learning approach using multiple classifiers for diabetes diagnosis based on the Pima Indians Diabetes Dataset (PIDD). The proposed ensemble voting classifier amalgamates five machine learning algorithms: Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forests (RF), and XGBoost. We obtained the individual model accuracies and used the ensemble method to improve accuracy. The proposed approach uses a pre-processing stage of standardization and imputation and applies the Local Outlier Factor (LOF) to remove data anomalies. The model was evaluated using sensitivity, specificity, and accuracy criteria. With a reported accuracy of 81%, the proposed approach shows promise compared to prior classification techniques. [ABSTRACT FROM AUTHOR]

Background: Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM. Objectives: We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators. Methods: In this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms. Patients: Regarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling. Results: The indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185. Conclusion: This work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients. [ABSTRACT FROM AUTHOR]

IntroductionLow socioeconomic status affects not only diagnosis rates and therapy of patients with diabetes mellitus but also their health behavior. Our primary goal was to examine diagnosis rates and therapy of individuals with diabetes living in Ormánság, one of the most deprived areas in Hungary and Europe. Our secondary goal was to examine the differences in lifestyle factors and cancer screening participation of patients with diagnosed and undiagnosed diabetes compared to healthy participants.MethodsOur study is a cross-sectional analysis using data from the “Ormánság Health Program”. The “Ormánság Health Program” was launched to improve the health of individuals in a deprived region of Hungary. Participants in the program were coded as diagnosed diabetes based on diagnosis by a physician as a part of the program, self-reported diabetes status, and self-reported prescription of antidiabetic medication. Undiagnosed diabetes was defined as elevated blood glucose levels without self-reported diabetes and antidiabetic prescription. Diagnosis and therapeutic characteristics were presented descriptively. To examine lifestyle factors and screening participation, patients with diagnosed and undiagnosed diabetes were compared to healthy participants using linear regression or multinomial logistic regression models adjusted for sex and age.ResultsOur study population consisted of 246 individuals, and 17.9% had either diagnosed (n=33) or undiagnosed (n=11) diabetes. Metformin was prescribed in 75.8% (n=25) of diagnosed cases and sodium-glucose cotransporter-2 inhibitors (SGLT-2) in 12.1% (n=4) of diagnosed patients. After adjustment, participants with diagnosed diabetes had more comorbidities (adjusted [aOR]: 3.50, 95% confidence interval [95% CI]: 1.34–9.18, p

Differences between capillary and venous glucose concentrations have been reported in the past. In continuous glucose monitoring (CGM) system performance studies, comparator measurements are often performed in venous samples, despite CGM systems typically aiming at providing capillary-like values. In this study, differences between venous, capillary and interstitial glucose concentrations, measured with a laboratory analyzer, a self-monitoring of blood glucose (SMBG) system and an intermittent-scanning CGM system were investigated in subjects without diabetes after glucose load.

Diabetes is a dreaded disease that can be identified by elevated blood glucose levels in the blood, and undiagnosed diabetes can cause a host of related complications, such as retinopathy and nephropathy. In terms of type, the main categories are type 1 diabetes (T1DM), type 2 diabetes (T2DM) and gestational diabetes mellitus (GDM). Machine learning models and metaheuristic optimization algorithms can play an important role in the early detection, diagnosis and treatment of this disease. To this end, we propose AHDHS-Stacking, an ensemble learning framework for diabetes mellitus classification and diagnosis that is based on the harmony search (HS) algorithm and stacking and includes two stages of feature selection and optimization of base-learner combinations. To improve the model’s overall performance, the average performance of all base learners is used as the feature selection target, and an adaptive hyperparameter strategy is used to accelerate the iterative process. HS is then used to optimize to find the best combination of base learners, which improves model performance while reducing complexity. Following that, we conducted experiments on the Pima Indians Diabetes (PID) dataset and the Chinese and Western Medicine Diabetes (CWMD) dataset, achieving accuracy of 93.09%, precision of 93.22%, recall of 91.60% , F-measure of 92.25%, and MCC of 84.79% on PID dataset, which is better than all benchmark models and validated the model’s validity. CWMD dataset experimental results showed that AHDHS-Stacking screened for key features such as age, gender, urinary glucose, fasting glucose, BMI and cholesterol, and can be used as a practical and accurate method for early diabetes prediction.

Artificial Intelligence (AI) has experienced rapid advancements in recent years, facilitating the creation of innovative, sustainable tools and technologies across various sectors. Among these applications, the use of AI in healthcare, particularly in the diagnosis and management of chronic diseases like diabetes, has shown significant promise. Automated Machine Learning (AutoML), with its minimally invasive and resource-efficient approach, promotes sustainability in healthcare by streamlining the process of predictive model creation. This research paper delves into advancements in AutoML for predictive modeling in diabetes diagnosis. It illuminates their effectiveness in identifying risk factors, optimizing treatment strategies, and ultimately improving patient outcomes while reducing environmental footprint and conserving resources. The primary objective of this scholarly inquiry is to meticulously identify the multitude of factors contributing to the development of diabetes and refine the prediction model to incorporate these insights. This process fosters a comprehensive understanding of the disease in a manner that supports the principles of sustainable healthcare. By analyzing the provided dataset, AutoML was able to select the most fitting model, emphasizing the paramount importance of variables such as Glucose, BMI, DiabetesPedigreeFunction, and BloodPressure in determining an individual's diabetic status. The sustainability of this process lies in its potential to expedite treatment, reduce unnecessary testing and procedures, and ultimately foster healthier lives. Recognizing the importance of accuracy in this critical domain, we propose that supplementary factors and data be rigorously evaluated and incorporated into the assessment. This approach aims to devise a model with enhanced accuracy, further contributing to the efficiency and sustainability of healthcare practices. [ABSTRACT FROM AUTHOR]

Background: Validation studies of diabetes definitions using nationwide healthcare databases are scarce. We evaluated the validity of diabetes definitions using disease codes and antidiabetic drug prescriptions in the Japanese Diagnosis Procedure Combination (DPC) data via medical chart review. Methods: We randomly selected 500 records among 15,334 patients who participated in the Japan Public Health Center-Based Prospective Study for the Next Generation in Yokote City and who had visited a general hospital in Akita between October 2011 and August 2018. Of the 500 patients, 98 were linked to DPC data; however, only 72 had sufficient information in the medical chart. Gold standard confirmation was performed by board-certified diabetologists. DPC-based diabetes definitions were based on the International Classification of Diseases, 10th Revision codes and antidiabetic prescriptions. Sensitivity, specificity, and the positive and negative predictive values (PPV and NPV, respectively) of DPC-based diabetes definitions were evaluated. Results: Of 72 patients, 23 were diagnosed with diabetes using chart review; 19 had a diabetes code, and 13 had both a diabetes code and antidiabetic prescriptions. The sensitivity, specificity, PPV, and NPV were 89.5% (95% confidence interval [CI], 66.9–98.7%), 96.2% (95% CI, 87.0–99.5%), 89.5% (95% CI, 66.9–98.7%), and 96.2% (95% CI, 87.0–99.5%), respectively, for (i) diabetes codes alone; 89.5% (95% CI, 66.9–98.7%), 94.3% (95% CI, 84.3–98.8%), 85.0% (95% CI, 62.1–96.8%), and 96.2% (95% CI, 86.8–99.5%) for (ii) diabetes codes and/or prescriptions; 68.4% (95% CI, 43.4–87.4%), 100% (95% CI, 93.3–100%), 100% (95% CI, 75.3–100%), and 89.8% (95% CI, 79.2–96.2%) for (iii) both diabetes codes and prescriptions. Conclusion: Our results suggest that DPC data can accurately identify diabetes among inpatients using (i) diabetes codes alone or (ii) diabetes codes and/or prescriptions.

Explainable artificial intelligence (XAI) tools are used to enhance the applications of existing artificial intelligence (AI) technologies by explaining their execution processes and results. In most past research, XAI tools and techniques are typically applied to only the inference part of the AI application. This study proposes a systematic approach to enhance the explainability of AI applications in healthcare. Several AI applications for type 2 diabetes diagnosis are taken as examples to illustrate the applicability of the proposed methodology. According to experimental results, the XAI tools and technologies in the proposed methodology were more diverse than those in the past research. In addition, an artificial neural network was approximated to a simpler and more intuitive classification and regression tree (CART) using local interpretable model-agnostic explanation (LIME). The extracted rules were used to recommend actions to the users to restore their health.

The diagnosis of diabetes, a prevalent global health condition, is crucial for preventing severe complications. In recent years, there has been a growing effort to develop intelligent diagnostic systems for diabetes utilizing machine learning (ML) algorithms. Despite these efforts, achieving high accuracy rates using such systems remains a significant challenge. Recent advancements in ensemble ML methods offer promising opportunities for early detection of diabetes, as they are known to be faster and more cost-effective than traditional approaches. Therefore, this study proposes a practical framework for diagnosing diabetes that involves three stages. The data preprocessing stage encompasses several crucial tasks, including handling missing values, identifying outliers, balancing the data, normalizing the data, and selecting relevant features. Subsequently, the hyperparameters of the ML algorithms are fine-tuned using grid search to improve their performance. In the final stage, the framework employs ensemble techniques such as bagging, boosting, and stacking to combine multiple ML algorithms and further enhance their predictive capability. Pima Indians Diabetes Database open-access dataset was used to test the performance of the proposed models. The experimental results of this framework indicate the superiority of ensemble methods in diagnosing diabetes compared to individual ML models. The stacking method achieved the best accuracy among the ensemble methods, with the stacked random forest (RF) and support vector machine (SVM) model attaining an accuracy of 97.50%. Among the bagging methods, the RF model yielded the highest accuracy, while among the boosting methods, eXtreme Gradient Boosting (XGB) model achieved the highest accuracy rates of 97.20% and 97.10%, respectively. Moreover, our proposed framework outperforms other ML models as confirmed by the comparison. The study has demonstrated that ensemble methods are crucial for accurate diabetes diagnosis, enabling early detection through efficient preprocessing and calibrated models. [ABSTRACT FROM AUTHOR]

Machine Learning techniques have gained prominence in medical diagnosis due to their ability to uncover patterns in complex data-sets, thereby giving accurate disease classification. In this study, we mainly focus on the application of two widely used Machine Learning algorithms, Logistic Regression and K-Nearestneighbors(KNN), for the purpose of distinguishing patients with diabetes from those without. Our research aims to shed light on the comparative accuracy and performance of these algorithms in a medical context. The methodology section outlines experimental setup, detailing data processing, algorithm training and testing procedures. A comprehensive data-set comprising medical attributes is utilized for evaluation and accuracy metrics are employed to quantify the performance of the algorithms. Results has shown efficacy of both the algorithms and our findings showcase the strengths and limitations of each approach, contributing on the applicability in medical decision making. By offering a nuanced comparison, we illuminate a path for more robust and accurate disease identification techniques, further enhancing patient care and medical outcomes. [ABSTRACT FROM AUTHOR]

Diabetes is a global health issue affecting millions of people and is related to high morbidity and mortality rates. Current diagnostic methods are primarily invasive, involving blood sampling, which can lead to infection and increased patient stress. As a result, there is a growing need for noninvasive diabetes diagnostic methods that are both accurate and fast. High measurement accuracy and fast measurement time are essential for effective noninvasive diabetes diagnosis; these can be achieved using diffuse speckle contrast analysis (DSCA) systems and artificial intelligence algorithms. In this study, we use a machine learning algorithm to analyze rat blood flow signals measured using a DSCA system with simple operation, easy fabrication, and fast measurement for helping diagnose diabetes. The results confirmed that the machine learning algorithm for analyzing blood flow oscillation data shows good potential for diabetes classification. Furthermore, analyzing the blood flow reactivity test revealed that blood flow signals can be quickly measured for diabetes classification. Finally, we evaluated the influence of each blood flow oscillation data on diabetes classification through feature importance and Pearson correlation analysis. The results of this study should provide a basis for the future development of hemodynamic-based disease diagnostic methods.

Objective: To determine whether nailfold capillary images, acquired using video capillaroscopy, can provide diagnostic information about diabetes and its complications. Research Design and Methods: Nailfold video capillaroscopy was performed in 120 adult patients with and without type 1 or type 2 diabetes, and with and without cardiovascular disease. Nailfold images were analyzed using convolutional neural networks, a deep learning technique. Cross‐validation was used to develop and test the ability of models to predict five5 prespecified states (diabetes, high glycosylated hemoglobin, cardiovascular event, retinopathy, albuminuria, and hypertension). The performance of each model for a particular state was assessed by estimating areas under the receiver operating characteristics curves (AUROC) and precision recall curves (AUPR). Results: A total of 5236 nailfold images were acquired from 120 participants (mean 44 images per participant) and were all available for analysis. Models were able to accurately identify the presence of diabetes, with AUROC 0.84 (95% confidence interval [CI] 0.76, 0.91) and AUPR 0.84 (95% CI 0.78, 0.93), respectively. Models were also able to predict a history of cardiovascular events in patients with diabetes, with AUROC 0.65 (95% CI 0.51, 0.78) and AUPR 0.72 (95% CI 0.62, 0.88) respectively. Conclusions: This proof‐of‐concept study demonstrates the potential of machine learning for identifying people with microvascular capillary changes from diabetes based on nailfold images, and for possibly identifying those most likely to have diabetes‐related complications. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new colorimetric sensor was developed using nanofiber aerogels as solid carrier. • The sensor exhibited a 3D multi-level pore structure, facilitating gas mass transfer. • The sensor demonstrated a rapid response (30 s) with good anti-interference ability. • Accuracy and feasibility of sensing method was confirmed by comparison with GC–MS. Polyimide nanofiber aerogels loaded with thymol blue and hydroxylamine sulfate (TB-HAS@PI NFAs) were synthesized and employed as a novel sensor for colorimetric detection of breath acetone (BrAce). The enhancement of 3D multi-level pore structure of TB-HAS@PI NFAs for sensing performance was elucidated through systematical characterization results. Because the designed sensing system relied on the specific reaction of HAS with acetone, TB-HAS@PI NFAs exhibited a more sensitive response ability to acetone, ensuring that the colorimetric sensing detection will not be interfered by other coexisting gas in exhaled breath. Moreover, the sensor demonstrated a rapid response, with color changes quantitatively measured within 30 s of exposure to acetone gas. Importantly, the feasibility and accuracy of established method were confirmed through comparison with GC–MS. This research provided a promising strategy for daily health management of healthy people and diabetics. [ABSTRACT FROM AUTHOR]

The diagnosis and management of diabetes require frequent monitoring of blood sugar levels. Prolonged exposure of most of the monosaccharides in the bloodstream results in the glycation of hemoglobin. This glycated hemoglobin (HbA1c) based test plays an important role to avoid diabetic complications. However, noninvasive estimation of HbA1c is a very new, promising, and challenging topic in modern bioengineering scopes. The purpose of this study is to develop and verify mathematical models in order to quantify the glycated hemoglobin in-vivo percentage non-invasively. This research utilized photon diffusion theory to develop the finger models and genetic symbolic regression methods to solve the models to estimate the level of glycated hemoglobin in the blood. The validation of these models with human participants indicated a high degree of correlation (0.887 and 0.907 Pearson's r value), and high precision (2.56% and 2.96% coefficient of variation (%CV)) for transmission and reflection type noninvasive digital volume pulse-based signals. This research will be a breakthrough for the application of noninvasive HbA1c estimation. [ABSTRACT FROM AUTHOR]

As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disease is turned into a significant challenge for the machine learning researchers. This paper aims to design a robust model for diagnosis of diabetes using a hybrid approach of Chaotic-Jaya (CJaya) algorithm with Extreme Learning Machine (ELM), which is named as CJaya-ELM. In this paper, Jaya algorithm with Chaotic learning approach is used to optimize the random parameters of ELM classifier. Here, to assess the efficacy of the designed model, Pima Indian diabetes dataset is considered. Here, the designed model CJaya-ELM, has been compared with basic ELM, Teaching Learning Based Optimization algorithm (TLBO) optimized ELM (TLBO-ELM), Multi-Layer Perceptron (MLP), Jaya algorithm optimized MLP (Jaya-MLP), TLBO algorithm optimized MLP (TLBO-MLP) and CJaya algorithm optimized MLP models. CJaya-ELM model resulted in the highest testing accuracy of 0.9687, sensitivity of 1, specificity of 0.9688 with 0.9782 area under curve (AUC) value. Results reveal that CJaya-ELM model effectively classifies both the positive and negative samples of Pima and outperforms the competitors.

Diabetes is the major health concerns across the whole world. Increasing at a rapid rate, this global epidemic is affecting a large portion of the populace everywhere. India having the highest proportion of diabetic patients, also becoming the 'diabetes capital of the world'. Early detection of diabetes could help to prevent or postpone its onset by taking appropriate preventive measures, including the initiation of lifestyle changes. AI-based diabetes diagnostic solutions are the new trends that companies are embracing to ensure smooth diabetes care. They provide diagnostic programs to detect complex ailments from medical images. By annotating lesions and abnormalities, these programs assist medical scholars and even non-specialists to have a faster diagnosis with much more accuracy. India faces a chronic disease risk burden. Not just this, many people especially those in the age group of 25 to 40 are also being diagnosed with diabetes & cardiovascular diseases according to Journal of the American Medical Association. This research addresses the existing scenario of AI based Diabetes Diagnostic services available in Maharashtra & Karnataka states in India. Firstly, the geographic location of Maharashtra & Karnataka and its existing Healthcare & Diagnostic status are explained. Then, various policies initiated by the government and the healthcare sectors of private ventures are described. Finally, the future prospects of AI based Healthcare Diagnostics in the two states along with possible strategies to address the barriers and issues are mentioned in this paper. [ABSTRACT FROM AUTHOR]

Abstract Background Measurement of gylcated hemoglobin A1c (HbA1c) plays a central role in monitoring quality of antidiabetic therapy and in the diagnosis of diabetes. Several studies report increased levels of HbA1c in nondiabetic elderly. However, this observation did not reach incorporation into daily clinical practice or the respective guidelines. The present study aimed to evaluate HbA1c levels in relation to age in two independent population-based cohorts and to derive age-specific reference intervals. Methods Four thousand two hundred sixty three participants from the Study of Health in Pomerania (SHIP-0) and 4402 participants from the independent study SHIP-Trend were included. HbA1c was determined by means of high-performance liquid chromatography. Multivariable linear regression models were performed. Reference intervals for HbA1c were determined. Results Reference intervals were derived from a healthy subpopulation with the upper reference limit (URL) for HbA1c of 42.1 mmol/Mol (6.0%) for individuals aged 20–39 years increasing to 43.2 mmol/Mol (6.1%) for individuals aged 40–59 years. For people aged ≥60 years the URL was 47.5 mmol/Mol (6.5%). In both study populations an increase in HbA1c with age was observed. ANOVA revealed up to 8.5 mmol/Mol (0.77%) or 7.3 mmol/Mol (0.68%) higher estimated mean levels of HbA1c in the oldest compared to the youngest age group in SHIP-0 or SHIP-trend, respectively. Linear regression analyses confirmed the positive associations of HbA1c with age which was independent of BMI Conclusion The present study confirmed the previously observed increase of HbA1c with increasing age in non-diabetic individuals. As a consequence age-dependent reference values for HbA1c were derived from two large and well defined reference populations. Implementation of them into daily practice may improve patient care and diagnosis of diabetes and reduce the risk of misdiagnosis and subsequent overtreatment of diabetes in elderly patients.

As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diabetes disease over worldwide and it is anticipated that by 2025 this estimation can cross 380 million. So, the proper and quick diagnosis of this disease is turned into a significant challenge for the machine learning researchers. This paper aims to design a robust model for diagnosis of diabetes using a hybrid approach of Chaotic-Jaya (CJaya) algorithm with Extreme Learning Machine (ELM), which is named as CJaya-ELM. In this paper, Jaya algorithm with Chaotic learning approach is used to optimize the random parameters of ELM classifier. Here, to assess the efficacy of the designed model, Pima Indian diabetes dataset is considered. Here, the designed model CJaya-ELM, has been compared with basic ELM, Teaching Learning Based Optimization algorithm (TLBO) optimized ELM (TLBO-ELM), Multi-Layer Perceptron (MLP), Jaya algorithm optimized MLP (Jaya-MLP), TLBO algorithm optimized MLP (TLBO-MLP) and CJaya algorithm optimized MLP models. CJaya-ELM model resulted in the highest testing accuracy of 0.9687, sensitivity of 1, specificity of 0.9688 with 0.9782 area under curve (AUC) value. Results reveal that CJaya-ELM model effectively classifies both the positive and negative samples of Pima and outperforms the competitors. [ABSTRACT FROM AUTHOR]

Using expert systems in the medical field has been practiced continuously for the past decades. There are attempts of using expert systems for a diabetes diagnosis. In this paper, we go further by proposing an expert system that not only diagnoses diabetes but also recommends the right medication depending on the location where the patient lives and on the symptoms of the patient and other effective factors. This system can be very helpful to many diabetic patients, especially to those who are not aware of their disease type or how to control it. The system outputs a list of names of locally available brand names of medications that suit the diabetes type of the patient and that do not pose any danger to the health of the patients according to their symptoms, effective factors, and results of the patients’ medical tests. Our expert system is capable of reasoning using either forward chaining or backward chaining. The rules in the knowledge base are collected from several medical textbooks and articles published in scientific journals, periodicals, and international conferences. To verify the content of the knowledge base, a medical expert and a pharmacist working in Kuwait were consulted. [ABSTRACT FROM AUTHOR]

Background: Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM., Objectives: We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators., Methods: In this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms., Patients: Regarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling., Results: The indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185., Conclusion: This work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lv, Luo, Huang, Guo, Bai, Yan, Jiang, Zhang, Jing, Chen and Li.)

Diabetes is a serious chronic disease. The importance of clinical decision support systems (CDSSs) to diagnose diabetes has led to extensive research efforts to improve the accuracy, applicability, interpretability, and interoperability of these systems. However, this problem continues to require optimization. Fuzzy rule-based systems are suitable for the medical domain, where interpretability is a main concern. The medical domain is data-intensive, and using electronic health record data to build the FRBS knowledge base and fuzzy sets is critical. Multiple variables are frequently required to determine a correct and personalized diagnosis, which usually makes it difficult to arrive at accurate and timely decisions. In this paper, we propose and implement a new semantically interpretable FRBS framework for diabetes diagnosis. The framework uses multiple aspects of knowledge-fuzzy inference, ontology reasoning, and a fuzzy analytical hierarchy process (FAHP) to provide a more intuitive and accurate design. First, we build a two-layered hierarchical and interpretable FRBS; then, we improve this by integrating an ontology reasoning process based on SNOMED CT standard ontology. We incorporate FAHP to determine the relative medical importance of each sub-FRBS. The proposed system offers numerous unique and critical improvements regarding the implementation of an accurate, dynamic, semantically intelligent, and interpretable CDSS. The designed system considers the ontology semantic similarity of diabetes complications and symptoms concepts in the fuzzy rules' evaluation process. The framework was tested using a real data set, and the results indicate how the proposed system helps physicians and patients to accurately diagnose diabetes mellitus.

Artificial intelligence (AI) applications based on deep learning for diagnosing type-II diabetes are sometimes difficult to understand and communicate even as patients are eager to understand the rationale behind the diagnostic results. Accordingly, recent studies have used multiple simple rules to adequately explain the diagnostic process and results to patients. However, this can cause patient confusion as the rules vary. Hence, this study proposes a deep neural network (DNN) with random forest (RF) and modified random forest incremental interpretation (MRFII) approach for diagnosing diabetes. This method first entails constructing a DNN to predict the probability of a patient having diabetes. To make the prediction result explainable, an RF is built to explain the process and results in terms of multiple simple decision rules. Additionally, to eliminate patient confusion, the MRFII is proposed to sort and aggregate the decision rules for a specific patient. A certainty mechanism is also established to feed back the explanation results from RF to improve the effectiveness of the DNN. The proposed method was applied to a diabetes dataset from the National Institute of Diabetes and Digestive and Kidney Diseases, and the results showed that this approach provided a more concise and accurate explanation than existing explainable artificial intelligence (XAI) techniques for the same purpose. • The deep neural network-random forest with modified incremental interpretation approach is proposed for diabetes diagnosis. • A deep neural network is constructed to predict the probability of a patient having diabetes. • A random forest is built to explain the prediction process and results in terms of simpler decision rules. • Patient confusion is avoided by applying modified random forest incremental interpretation. [ABSTRACT FROM AUTHOR]

Diabetes is a dreaded disease that can be identified by elevated blood glucose levels in the blood, and undiagnosed diabetes can cause a host of related complications, such as retinopathy and nephropathy. In terms of type, the main categories are type 1 diabetes (T1DM), type 2 diabetes (T2DM) and gestational diabetes mellitus (GDM). Machine learning models and metaheuristic optimization algorithms can play an important role in the early detection, diagnosis and treatment of this disease. To this end, we propose AHDHS-Stacking, an ensemble learning framework for diabetes mellitus classification and diagnosis that is based on the harmony search (HS) algorithm and stacking and includes two stages of feature selection and optimization of base-learner combinations. To improve the model's overall performance, the average performance of all base learners is used as the feature selection target, and an adaptive hyperparameter strategy is used to accelerate the iterative process. HS is then used to optimize to find the best combination of base learners, which improves model performance while reducing complexity. Following that, we conducted experiments on the Pima Indians Diabetes (PID) dataset and the Chinese and Western Medicine Diabetes (CWMD) dataset, achieving accuracy of 93.09%, precision of 93.22%, recall of 91.60% , F-measure of 92.25%, and MCC of 84.79% on PID dataset, which is better than all benchmark models and validated the model's validity. CWMD dataset experimental results showed that AHDHS-Stacking screened for key features such as age, gender, urinary glucose, fasting glucose, BMI and cholesterol, and can be used as a practical and accurate method for early diabetes prediction. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is a severe chronic disease that affects human health and has a high prevalence worldwide. Research has shown that half of the diabetic people throughout the world are unaware that they have DM and its complications are increasing, which presents new research challenges and opportunities. In this paper, we propose a preemptive diagnosis method for diabetes mellitus (DM) to assist or complement the early recognition of the disease in countries with low medical expert densities. Diabetes data are collected from the Zewditu Memorial Hospital (ZMHDD) in Addis Ababa, Ethiopia. Light Gradient Boosting Machine (LightGBM) is one of the most recent successful research findings for the gradient boosting framework that uses tree-based learning algorithms. It has low computational complexity and, therefore, is suited for applications in limited capacity regions such as Ethiopia. Thus, in this study, we apply the principle of LightGBM to develop an accurate model for the diagnosis of diabetes. The experimental results show that the prepared diabetes dataset is informative to predict the condition of diabetes mellitus. With accuracy, AUC, sensitivity, and specificity of 98.1%, 98.1%, 99.9%, and 96.3%, respectively, the LightGBM model outperformed KNN, SVM, NB, Bagging, RF, and XGBoost in the case of the ZMHDD dataset.

Knowledge-Intensive Case-Based Reasoning Systems (KI-CBR) mainly depend on ontologies. Ontology can play the role of case-base knowledge. The combination of ontology and fuzzy logic reasoning is critical in the medical domain. Case-base representation based on fuzzy ontology is expected to enhance the semantic and storage of CBR knowledge-base. This paper provides an advancement to the research of diabetes diagnosis CBR by proposing a novel case-base fuzzy OWL2 ontology (CBRDiabOnto). This ontology can be considered as the first fuzzy case-base ontology in the medical domain. It is based on a case-base fuzzy Extended Entity Relation (EER) data model. It contains 63 (fuzzy) classes, 54 (fuzzy) object properties, 138 (fuzzy) datatype properties, and 105 fuzzy datatypes. We populated the ontology with 60 cases and used SPARQL-DL for its query. The evaluation of CBRDiabOnto shows that it is accurate, consistent, and cover terminologies and logic of diabetes mellitus diagnosis.

Highlights • The PIMA Indian Type-2 diabetes dataset is used. • Pre-processing techniques are combined together to access high-quality data. • Significant features are found using SVM. • Four bi-objective meta-heuristics are employed to maximize the accuracy and to minimize the number of selected features. • The 10-fold cross validation method is used to validate the constructed model. Abstract Diabetes mellitus is a medical condition examined by data miners for reasons such as significant health complications in affected people, the economic impact on healthcare networks, and so on. In order to find the main causes of this disease, researchers look into the patient's lifestyle, hereditary information, etc. The goal of data mining in this context is to find patterns that make early detection of the disease and proper treatment easier. Due to the high volume of data involved in therapeutic contexts and disease diagnosis, provision of the intended treatment method become almost impossible over a short period of time. This justifies the use of pre-processing techniques and data reduction methods in such contexts. In this regard, clustering and meta-heuristic algorithms maintain important roles. In this paper, a method based on the k-means clustering algorithm is first utilized to detect and delete outliers. Then, in order to select significant and effective features, four bi-objective meta-heuristic algorithms are employed to choose the least number of significant features with the highest classification accuracy using support vector machines (SVM). In addition, the 10-fold cross validation (CV) method is used to validate the constructed model. Using real case data, it is concluded that the multi-objective firefly (MOFA) and multi-objective imperialist competitive algorithm (MOICA) with a 100% classification accuracy outperform the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) with the accuracies of 98.2% and 94.6%, respectively. [ABSTRACT FROM AUTHOR]

Highlights • A toenail-based non-invasive method for diagnosing type-2 diabetes was developed. • Al, Cs, Ni, V, Zn in toenails were significantly different for diabetes patients. • Toenail concentrations of 22 elements were used for machine learning modeling. • A random forest model correctly classified 7 out of 9 samples, with AUC = 0.90. Abstract Described for the first time is the use of elemental analysis of diabetic toenails and machine learning techniques for the robust classification of type-2 diabetes. Aluminum, Cs, Ni, V and Zn concentrations in toenails were found to be significantly (p < 0.05) different between healthy volunteers and type-2 diabetes patients. Seven different machine learning algorithms were then studied to develop a non-invasive diagnostic method using concentrations of twenty-two elements in toenails, and personal information such as age, gender and smoking history as features. Models were enhanced through feature selection and two different ensembling strategies. The performance of forty-six distinct machine learning models were compared on resampled training data and testing data. A random forest model, trained with concentrations of Al, Ba, Ca, Cr, Cs, Cu, Fe, Mg, Mn, Ni, P, Pb, Rb, S, Sb, Se, Sn, Sr, V and Zn (µg g−1), as well as information on age, gender and smoking history, had an area under the receiver operating characteristic curve (AUC) of 0.73 on the training data, and correctly predicted seven out of nine test samples (including control and disease), with an AUC of 0.90. The results at this stage of the research prove the concept of combining elemental analysis of toenails and machine learning techniques for non-invasively diagnosing type-2 diabetes. With proper sample collection and shipping, mobility-limited patients may be able to mail toenail samples for analysis and monitor their type-2 diabetes over time. A health clinic equipped with common instrumentation, software and trained algorithms similar to those used in the present study may be able to serve a large number of patients from across the world. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

In the medical field, experts' knowledge is based on experience, theoretical knowledge and rules. Case-based reasoning is a problem-solving paradigm which is based on past experiences. For this purpose, a large number of decision support applications based on CBR have been developed. Cases retrieval is often considered as the most important step of case-based reasoning. In this article, we integrate fuzzy logic and data mining to improve the response time and the accuracy of the retrieval of similar cases. The proposed Fuzzy CBR is composed of two complementary parts; the part of classification by fuzzy decision tree realized by Fispro and the part of case-based reasoning realized by the platform JColibri. The use of fuzzy logic aims to reduce the complexity of calculating the degree of similarity that can exist between diabetic patients who require different monitoring plans. The results of the proposed approach are compared with earlier methods using accuracy as metrics. The experimental results indicate that the fuzzy decision tree is very effective in improving the accuracy for diabetes classification and hence improving the retrieval step of CBR reasoning. [ABSTRACT FROM AUTHOR]

Wearable devices for digital continuous glucose monitoring (CGM) have attracted great attention as a new paradigm medical device for diabetes management. However, the relatively inaccurate performance and instability of CGM devices have limited their wide applications in the clinic. Here, we developed hyaluronate (HA) modified Au@Pt bimetallic electrodes for long-term accurate and robust CGM of smart contact lens. After glucose oxidation reaction, the bimetallic electrodes facilitated the rapid decomposition of hydrogen peroxide and charge transfer for robust CGM. The passivation of Au@Pt bimetallic electrode with branch-type thiolated HA prevented the dissolution of Au electrode by chloride ions in tears. In diabetic and normal rabbits, the smart contact lens with HA-Au@Pt bimetallic electrodes enabled the high correlation (ρ = 0.88) CGM with 98.6% clinically acceptable data for 3 weeks. Taken together, we could confirm the feasibility of our smart contact lens for long-term CGM for further clinical development. [Display omitted] • The remarkable sensitivity and stability of glucose sensor by bimetallic HA-Au@Pt electrode. • The successful integration of a glucose sensor, a chip, and a wireless antenna in a contact lens. • A long-term stable wireless smart contact lens for robust digital diabetes diagnosis. [ABSTRACT FROM AUTHOR]

Abstract Background Diabetes is a global public health issue. It is associated with significant disability, morbidity and mortality risks and substantial healthcare costs. Of great concern is the fact that its prevalence is rising, particularly amongst the young, while epidemiological data regarding the incidence, prevalence and complications of early-onset type 2 diabetes is noted to be sparse. Methods We used data from the baseline wave of Ten to Men, a national cohort study of Australian males, to investigate the social and health-related correlates of Australian males aged 18–49 years reporting being diagnosed with diabetes. Results The estimated prevalence of a self-reported diabetes diagnosis amongst Australian males aged 18–49 years was 2.95 % (95 % CI: 2.54–3.43 %). Within this age group, approximately 75 % of those diagnosed with diabetes are expected to be living with a known diagnosis of type 2 diabetes; the remainder are expected to be living with type 1 diabetes. Of the 20 social and health-related factors considered, we found evidence to support the association of eighteen factors after adjusting for age and body mass index. The strongest correlates of reporting a diabetes diagnosis, associated with a ≥2-fold increase in the odds of reporting diabetes were being aged 35–49 years, being unemployed, being obese, seeing a doctor for a check-up more frequently, reporting comorbid high blood pressure or physical or mental health comorbidities and worse self-rated and physical health status. Conclusion Australian males aged 18–49 years who are living with a known diagnosis of diabetes are more likely to be socio-economically disadvantaged and suffer substantially worse health status than Australian males aged 18–49 years living without a diabetes diagnosis. Based on the associations detected in this study, older, single males living in regional areas who are socioeconomically disadvantaged, obese and/or who have other comorbidities may be an important subgroup to target for diabetes screening, disease management and prevention efforts.

Knowledge-Intensive Case Based Reasoning (KI-CBR) systems mainly depend on ontology. Using ontology as domain knowledge supports the implementation of semantically-intelligent case retrieval algorithms. The case-based knowledge must be encoded with the same concepts of the domain ontology. Standard medical ontologies, such as SNOMED CT (SCT), can play the role of domain ontology to enhance case representation and retrieval. This study has three stages. First, we propose an encoding methodology using SCT. Second, this methodology is used to encode the case-based knowledge. Third, all the used SCT concepts are collected in a reference set, and an OWL2 ontology of 550 pre-coordinated concepts is proposed. A diabetes diagnosis is chosen as a case study of our proposed framework. SCT is used to provide a pre-coordination concept coverage of ∼75% for diabetes diagnosis terms. Whereas, the uncovered concepts in SCT are proposed. The resulting OWL2 ontology will be used as domain knowledge representation in diabetes diagnosis CBR systems. The proposed framework is tested by using 60 real cases.

In view of the low sensitivity, high operating temperature and poor selectivity of acetone measurements, in this paper much effort has been paid to improve the performance of acetone sensors from three aspects: increasing the surface area of the material, improving the surface activity and enhancing gas diffusion. A hierarchical flower-like Pt-doped (1 wt %) 3D porous SnO2 (3DPS) material was synthesized by a one-step hydrothermal method. The micropores of the material were constructed by subsequent annealing. The results of the experiments show that the 3DPS-based sensor's response is strongly dependent on temperature, exhibiting a mountain-like response curve. The maximum sensor sensitivity (Ra/Rg) was found to be as high as 505.7 at a heating temperature of 153 °C and with an exposure to 100 ppm acetone. Additionally, at 153 °C, the sensor still had a response of 2.1 when exposed to 50 ppb acetone gas. The 3DPS-based sensor also has an excellent selectivity for acetone detection. The high sensitivity can be explained by the increase in the specific surface area brought about by the hierarchical flower-like structure, the enhanced surface activity of the noble metal nanoparticles, and the rapid diffusion of free-gas and adsorbed gas molecules caused by the multiple channels of the microporous structure.

Fuzzy classification rules are more interpretable and cope better with pervasive uncertainty and vagueness with respect to crisp rules. Because of this fact, fuzzy classification rules are extensively used in classification and decision support systems for disease diagnosis. But, most of the rule mining techniques failed to generate accurate and comprehensive fuzzy rules. This paper presents a hybrid decision support system based on Rough Set Theory (RST) and Bat optimization Algorithm (BA) called RST-BatMiner. It consists of two stages. In the first stage, redundant features have been removed from the data set through RST-based QUICK-REDUCT approach. In the second stage, for each class BA is invoked to generate fuzzy rules by minimizing proposed fitness function. Further, an Ada-Boosting technique is applied to the rules generated by BA to increase the accuracy rate of generated fuzzy rules. Moreover, to generate comprehensive fuzzy rules, a new ≠ (not equal) operator along with = (equal) operator is introduced into BA encoding scheme. The proposed RST-BatMiner builds consolidated fuzzy ruleset by learning the rules associated with each class separately. The proposed RST-BatMiner is experimented on six bench-mark datasets namely Pima Indians Diabetes, Wisconsin Breast Cancer, Cleveland Heart disease, iris, wine and glass, in order to validate its generalization capability. These experimental results show that except for wine dataset the proposed RST-BatMiner yields high accuracy and comprehensible ruleset when compared to other state-of-the-art bio-inspired based fuzzy rule miners and Fuzzy Rule Based Classification Systems (FRBCS) in the literature. In the case of wine dataset, the proposed RST-BatMiner yields second highest accuracy along with comprehensible ruleset. [ABSTRACT FROM AUTHOR]

Diabetes threatens a patient’s health and quality of life, whereas disease diagnosis itself could potentially serve as a teachable moment for initiating behavior change. This study assessed diabetes diagnosis as a possible teachable moment for screen-based sedentary behavior among US adults. The nationally representative sample (n = 3690) came from the 2005-2006 and 2011-2012 National Health and Nutrition Examination Survey. Self-reported hours spent on screen-based sedentary behavior (television/video watching, computer/digital device use) were measured by the Global Physical Activity Questionnaire. Diabetes/prediabetes was identified by fasting plasma glucose and the glycated hemoglobin test. Logistic regressions were conducted to examine the relationship between diabetes diagnosis and screen-based sedentary behavior, adjusting for individual characteristics and sampling design. Compared with those with undiagnosed diabetes, the adjusted values for prevalence of daily television/video watching ≥2 hours (77.45% vs 65.14%), computer/digital device use ≥1 hour (43.20% vs 36.52%), and total screen time (ie, television/video watching plus computer/digital device use) ≥3 hours (66.75% vs 45.78%) were all noticeably higher among adults with diagnosed diabetes, although only the difference in the prevalence of daily total screen time was significant at P < .05. No evidence was found regarding diabetes diagnosis as a teachable moment in reducing screen-based sedentary behavior in US adults. [ABSTRACT FROM AUTHOR]

Background: Diabetes is considered a civilization disease. It is now one of the major problems of the modern world. More than 123 million people suffer from it. About 3.5% of the general population suffer from this disease every year. This causes a huge health, social and economic problem. The reason for such a high incidence of illness is beyond the aging population, poor lifestyle: diet and low physical activity. Diabetes affects people of all ages. Having a disease means a very serious problem for the patient. It essentially changes its life, requires long-term treatment, proper care and lifestyle changes. Aim: Defining the knowledge level of POZ patients on the diagnosis and treatment of type 2 diabetes. Material and method: The study group consisted of patients reporting to the POZ clinic at the Independent Public Health Care Unit in Bychawa. The research group comprised 36 women and 37 men. The research tool was an author's questionnaire, which included a total of 47 questions, 11 of them metric, and the remaining 36 related to knowledge about diabetes. Questions were closed questions. The results of the study were statistically analyzed. The values of the measured measurable parameters are expressed by means of mean, standard deviation, minimum and maximum values, and non-measurable characteristics by means of numerical and interest. Standard deviation is a popular measure of dispersion reflecting the degree of dispersion of measurements around the arithmetic mean. The correlation between variables was used to correlate Sperman (R) rankings. The significance level p

TiO 2 nanoparticles functionalized In 2 O 3 nanowires are synthesized by spin coating and a thermal annealing process using In 2 O 3 nanowires synthesized by VLS mechanism and hydrothermally synthesized TiO 2 nanoparticles. This sensor exhibits high performance acetone gas detection at low temperatures and ethanol gas detection at high temperatures. Sub-ppm scale acetone gas detection is important when diagnosing diabetes since exhaled breath contains approximately 1.8–10 ppm of acetone gas for diabetes sufferers, whereas it contains under 0.8 ppm of acetone gas for those without diabetes. Some kinds of sensing factors are controlled to enhance the sensing performance of acetone sensors using TiO 2 nanoparticles functionalized In 2 O 3 nanowires. In this research, we fabricate an acetone sensor which has good selectivity and responses, and research its sensing mechanisms. [ABSTRACT FROM AUTHOR]