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After a brief review of electronic nose technology, the use of an Australian electronic nose to identify an unknown odour out of a set of known odours is described. Multivariate supervised learning is accomplished by applying Bayes' theorem to data from metal oxide semiconductor sensors responding to each of a number of target odours. An odour from an unknown source is then assigned a probability of membership of each of the training sets by applying either a Naïve Bayes algorithm to the deemed independent data from each sensor, or to a multinormal distribution of the data. A flat prior (equal probabilities of each outcome) is usually adopted, but for particular situations where one odour is known to predominate, then suitably weighted priors can be used. A source 'none of the above', which has a small likelihood covering the space of the possible sensor responses, is included for completeness. This also avoids the assignment to a source that has an extremely small probability but which is greater than that of any other source. Examples are given of a single source (detecting diabetes from a patient's breath), and three sources of unpleasant odours in a meat processing plant. The 18th century cleric Thomas Bayes gave his name to an elegant statement of the probability of an event – in this case identification of an odour – given some evidence: output from a number of metal oxide semiconductor sensors. Knowing the distributions of outputs for target odours, we assign the probabilities of an unknown odour. The greatest probability wins! (Image credit: E-nose Pty Ltd and D. B. Hibbert.) This article belongs to the 10th Anniversary Collection of RACI and AAS Award papers. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) poses a significant health challenge globally, necessitating accurate and timely diagnosis for effective management. Conventional diagnostic methods often struggle to address the multifaceted nature of diabetes and the requisite lifestyle adjustments. In this study, we propose a data-driven approach utilizing machine learning techniques to enhance diabetes diagnosis. By leveraging extensive patient attributes and medical records, machine learning algorithms can uncover intricate patterns and correlations. Our methodology, validated on the PIMA India dataset, demonstrates promising results. The random forest model achieved the highest accuracy of 87%, followed closely by gradient boost at 90%. Notably, XGBoost and CATBoost models attained a peak accuracy of 90.9%. These findings underscore the potential of machine learning in transforming diabetes diagnosis. Beyond improving diagnostic accuracy, our approach aims to guide individuals towards healthier lifestyles. Intelligent systems driven by machine learning hold promise for revolutionizing diabetes management, ultimately leading to better patient outcomes and more effective health care delivery. [ABSTRACT FROM AUTHOR]

Real time telemedicine model especially in the face of emergency has come to stay. In a remote health monitoring system, patient identification is given more consideration as a crucial security requirement. To highlight the difficulties and talk about how complete the answers are, more research is necessary. For real-time telemedicine monitoring systems, a novel authentication mechanism built on models is suggested. To modernize the pattern of the neighborhood hospital's operations, the adoption of the created model as a new authentication mechanism is suggested in the first phase. The creation and data gathering of the recently constructed model are discussed in the second phase to put the methods into context. Our authentication method uses two modalities namely text and visual chat. Key component suggested usability for test users is identified. Also, future simulations and implementations with privacy protection, authentication process ensured resultant applications was robust, scalable, persistent and high-level usability. 81.08% of the medical personnel and 87.21% of the patients agreed telemedicine system should be adopted due to its quick diagnosis during emergency, reduced referrals, strengthens patients' confidence, eased usage, enhanced proficiency and management with user-friendly interface have all become inherent benefits of proposed system. [ABSTRACT FROM AUTHOR]

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]

Introduction The German Diabetes Association recommends using sampling tubes with citrate and fluoride additives to diagnose diabetes by oral glucose tolerance test to inhibit glycolysis. The effect of different tubes on measurement results was assessed. Materials and Methods In a first study, an oral glucose tolerance test was performed on 41 participants without anamnestically known diabetes. Venous blood was sampled in two different tubes with citrate/fluoride additives from different manufacturers and one with only lithium-heparin additive. A second study with 42 participants was performed to verify the initial results with an adapted design, in which a third tube with citrate buffer was used, and glucose measurements were performed on two additional devices of another analyser model. Samples were centrifuged either immediately (<5 min incubation time) or after 20 min or 4 h. All glucose measurements were performed in plasma. Glucose concentrations in lithium-heparin tubes with<5 min incubation time served as baseline concentrations. Results In the first study, glucose concentrations in one of the citrate/fluoride tubes were similar to the baseline. In the other citrate/fluoride tube, markedly lower concentrations (approximately − 5 mg/dL (− 0.28 mmol/L)) were measured. This was reproduced in the verification study for the same analyser, but not with the other analyser model. Lithium-heparin tubes centrifuged after 20 and 240 min showed systematically lower glucose concentrations. Conclusions The results confirm that glycolysis can be effectively inhibited in citrate/fluoride-containing sampling tubes. However, glucose measurement results of one analyser showed a relevant negative bias in tubes containing liquid citrate buffer. [ABSTRACT FROM AUTHOR]

Background and Aim: In recent years, machine learning and evolutionary algorithms have drawn the attention of researchers and specialists in various fields, especially in healthcare, due to their practical applications in processing large datasets to provide valuable insights. Considering the increasing prevalence of diabetes and its rapid and accurate diagnosis being one of the most critical issues in medicine, significant concerns are faced by global communities worldwide. The present study was conducted with the aim of creating a diagnostic model based on evolutionary algorithms and machine learning to diagnose diabetes. Materials and Methods: This research based on the Indian Pima diabetes dataset presents a framework based on intelligent diabetes diagnosis. The proposed method consists of two main stages. The first stage involves a classification approach using K-nearest neighbors and random forest algorithms. The second stage includes a combined feature selection and classification approach to enhance the results of the first stage, utilizing grey wolf optimization, whale optimization, and particle swarm optimization algorithms for feature selection. Comparative analysis among different approaches is conducted through evaluation metrics such as accuracy, precision, recall, and F1-score. Results: After comparative comparisons among the proposed models, the random forest model based on the grey wolf optimization was selected and introduced as the final model with a prediction accuracy of 81.38%. Conclusion: The findings of this research indicate that the use of evolutionary algorithms alongside machine learning models can often enhance the efficiency and accuracy of diabetes diagnosis and its associated complications. [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]

This paper reports the synthesis and gas sensing performance of Au-decorated Sn3O4 (Au/Sn3O4) nanoflowers as gas-sensitive materials for acetone gas sensors. Sn3O4 nanoflowers were first synthesized via a hydrothermal reaction and then were decorated with Au nanoparticles by reducing the Au ions in HAuCl4. The phase structure, surface morphology, specific surface area, and surface chemical composition of the synthesized samples were systematically investigated. The morphological characterization confirmed the flower-like structures of Au/Sn3O4 samples, which were composed of Sn3O4 nanosheets and uniformly dispersed Au nanoparticles. The synthesized Sn3O4 and Au/Sn3O4 nanoflowers were applied as gas-sensitive materials in low-cost integrated microelectro-mechanical system-based acetone sensors. Among these, the Au/Sn3O4 sensors demonstrated superior performance, with a higher response (Rair/Rgas = 6.1) and faster response/recovery time (9 s/25 s) toward 5 ppm acetone gas at a low operating temperature of 160 °C compared to the Sn3O4 sensors. In addition, Au/Sn3O4 sensors also presented high selectivity to acetone gas under complex atmospheric conditions, with an extremely low detection limit of 50 ppb. The results suggest that the designed acetone gas sensors are expected for early screening and diagnosis of diabetes through monitoring the acetone gas in exhaled breath. [ABSTRACT FROM AUTHOR]

The applicability of a UK‐validated genetic risk score (GRS) was assessed in 158 participants in the Fremantle Diabetes Study Phase II diagnosed between 20 and <40 years of age with type 1 or type 2 diabetes or latent autoimmune diabetes of adults (LADA). For type 1 versus type 2/LADA, the area under the receiver operating characteristic curve (AUC) was highest for serum C‐peptide (0.93) and lowest for the GRS (0.66). Adding age at diagnosis and body mass index to C‐peptide increased the AUC minimally (0.96). The GRS appears of modest diabetes diagnostic value in young Australians. [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.

Diabetes mellitus is a common and serious disease that has been studied by many researchers. Pima Indians Diabetes Dataset is one of the most famous datasets in this field. This study aims to increase the accuracy of machine learning algorithms in diagnosing the disease and to reveal the patterns that enable early diagnosis of the disease by focusing on the pre-processing stages. The proposed hybrid model includes "filling in missing values ​​with KNN", "examining six different normalization methods for normalization" and "removing outliers with K-means" in the pre-processing stage. In the data classification stage, four algorithms C4.5, SVM, Naïve Bayes and KNN were examined and the best hybrid model was found. The performance evaluation of these models is based on accuracy. The results were compared with previous studies and had higher accuracy of 98.3% and 99.1% for (KNN + n5 + K-means + SVM) and (KNN + n4/n3 + K-means + KNN), respectively. Finally, we offer the conclusive notes and some suggestions for further study. [ABSTRACT FROM AUTHOR]

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 one of the common health problems with an increasing incidence worldwide. Diabetes is a chronic disease that can damage organs such as the eyes, heart, and kidneys, as well as cause mortality if not taken under control. Early diagnosis of diabetes is important in terms of preventing complications and increasing the quality of life. Machine learning techniques, which are widely used in the medical field, play the role of an intelligent decision support system that helps experts in the diagnosis of different diseases. This study includes classifications performed on the Pima Indian Diabetes dataset with six different machine learning techniques for the early diagnosis of diabetes. One of the main goals of the classifications carried out is to increase the prediction accuracy. In this study, fourteen different resampling methods were used on the dataset to increase the success of the classifiers. A total of ninety classifications were carried out without sampling and resampling for each machine learning model. The success of each classification process was reported with five different performance metrics. The highest performance was obtained with an accuracy of 96.296% in the classification using the Random Forest with the InstanceHardnessThreshold under-sampling technique. It was observed that resampling techniques generally increased the success of the classifiers and were more successful when used together with ensemble learning methods. Compared to the other similar studies in the literature, it was shown that the results obtained in this study were higher than the others. [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]

Diabetes is one of the most common, dangerous and costly diseases in the world today, which is increasing at an alarming rate. The use of data mining methods can help in the early diagnosis of diabetes, which prevents the progression of this disease and many of its complications such as cardiovascular disease, vision problems and kidney disease. Providing care and health services to people with diabetes provides useful information that can be used to identify, treat, follow-up care and even prevent diabetes. In this study, a new method is presented to improve the diagnosis and prevention of diabetes using data mining methods. In this research, the DBSCAN clustering algorithm is used to cluster the data. Then, using SVM, we classify the data to identify useful data, and finally, with the firefly algorithm, we increase the obtained data to increase we optimize performance with this algorithm. The results of this study indicate that the DBSCAN algorithm is more efficient than other clustering algorithms. Also, the SVM algorithm can achieve 98% accuracy, which compared to other data mining algorithms could achieve a higher accuracy percentage. [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]

Detecting 1 ppm acetone at high humidity is essential for a noninvasive diabetes diagnosis. Metal oxide gas sensors are a promising technology to achieve high sensitivity acetone monitoring. Here, we fabricated Pt-sensitized In2O3 nanotubes, and the gas-sensing performance was tested against eight gases. The fiber structure contributes to the uniform dispersion of Pt onto the In2O3. Pt-sensitized In2O3 nanotubes have lower optimal operating temperatures and higher sensitivity and selectivity than those of the In2O3 nanotubes. The 0.75 wt % Pt-In2O3 sensor has the maximum sensitivity (113) to 10 ppm acetone at 300 °C; the response and response time to 1 ppm acetone are 19.9 and 10 s, respectively. The response to 1 ppm acetone still has 9.83 at the relative humidity of 83%. It also has a low limit of detection (8.4 ppb) and good long-term stability (30 days). These results illustrate that Pt-sensitized In2O3 nanotubes have the potential for a noninvasive diabetes diagnosis. [ABSTRACT FROM AUTHOR]

ZnCo2O4/Polypyrrole (ZCO/PPy) and MnCo2O4/Polypyrrole (MCO/PPy) nanocomposites with different weight percentages were successfully prepared using the in situ chemical polymerization method. The comparative chemiresistive gas sensing performance of ZCO/PPy and MCO/PPy nanocomposites was studied toward methane (CH4), hydrogen sulfide (H2S), acetone (C3H6O), and carbon dioxide (CO2). Both the nanocomposites have shown better sensitivity toward acetone gas at room temperature than other gasses. Among the studied materials, MCO-30/PPy nanocomposite can detect acetone at 2.5 ppm level and be a choice for further studies related to medical perspective (diabetes diagnosis). The gas sensing mechanism in ZCO/PPy and MCO/PPy was explained based on the heterojunction formed between ZCO/MCO nanoparticles and PPy. [ABSTRACT FROM AUTHOR]

The diabetes is a critical disease from the small children to old age people. Due to improper diet and physical activities of the living population, obesity becomes prevalent in young generation. If we analyze self care of individual life, no man or women ready to spend their time for health care. It leads to problem like diabetes, blood pressure etc. Today is a busy world were robots and artificial machines ready to take care of human personal needs. Automatic systems help humans to manage their busy schedule. It motivates us to develop a diabetes motoring system for patients using IoT device in their body which monitors their blood sugar level, blood pressure, sport activities, diet plan, oxygen level, ECG data. The data are processed using feature selection algorithm called as particle swarm optimization and transmitted to nearest edge node for processing in 5G networks. Secondly, data are processed using DBN Layer. Thirdly, we share the diagnosed data output through the wireless communication such as LTE/5G to the patients connected through the edge nodes for further medical assistance. The patient wearable devices are connected to the social network. The Result of our proposed system is evaluated with some existing system. Time and Performance outperform than other techniques. [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]

The hole accumulation layer (HAL) configuration of p-type oxides (including Co 3 O 4) in ambient air causes intrinsically low gas response and hinders their promising applications in exhaled gas analysis. Herein, Sn and Ni co-doping has been proposed to trigger the response of chemiresistive Co 3 O 4 sensor toward acetone (biomarker of diabetes). Via incorporating 1 at% Sn and 0.5 at% Ni doping (Co 2.95 Sn 0.03 Ni 0.02 O 4), the response to 100 ppm acetone has been boosted ∼2 orders (from 1.24 to 125.6) at 70 °C, the limit of detection (LoD) has been reduced ∼4 times (from 47.9 to 12.4 ppb), the optimal operation temperature has been decreased from 130 °C to ∼70 °C. Various characterizations suggest that co-doping induced abundant surface asymmetric oxygen vacancy defects (Co-□-Ni, Sn-□-Ni), which facilitate the catalytic oxidation of acetone molecules at relatively low operation temperature. In addition to excellent reproducibility and long-term stability, Co 2.95 Sn 0.03 Ni 0.02 O 4 sensor could also operate under highly humid air background and reliably detect acetone concentration in exhaled breath at 150 °C, opening the opportunity for the practical application of p-type oxide sensors for diabetes diagnosis. [Display omitted] • Sn, Ni co-doped Co3O4 dramatically improves the response to acetone. • Sn, Ni co-doped Co3O4 drastically reduces optimal operation temperature (70 °C). • Humidity influence of Co2.95Sn0.03Ni0.02O4 could be suppressed at 150 °C. • Application of Co2.95Sn0.03Ni0.02O4 sensor for non-invasive diabetes monitoring. [ABSTRACT FROM AUTHOR]

Aims: Oral glucose tolerance tests (oGTT) are widely used for the diagnosis of diabetes. It is well known that the reproducibility of oGTT is poor and that a number of factors have an impact on the outcome of this diagnostic test. It appears as if one aspect, the oral glucose solution (OGS) used has not achieved much attention. Very little is published about this, despite the fact that apparently most often not a pure and freshly prepared glucose solution is used but a ready‐to‐use solution prepared by a (pharmaceutical) company. Methods: A literature search was performed to find respective publications. Results: It appears as if no or only a small number of not adequately designed clinical‐experimental studies have been performed comparing different OGS head‐to‐head. Conclusions: The composition of such OGS, including the excipients added to improve taste and smell, can have an impact on blood glucose increase after drinking the given OGS. Such factors can also have an impact on endogenous insulin secretion. If significant differences in the blood glucose excursions exist depending on which OGS is used, this calls for the use of a standardized OGS in oGTT to have a comparable outcome everywhere. [ABSTRACT FROM AUTHOR]

Diabetes is one of the most common, dangerous and costly diseases in the world today, which is increasing at an alarming rate. The use of data mining methods can help in the early diagnosis of diabetes, which prevents the progression of this disease and many of its complications such as cardiovascular disease, vision problems and kidney disease. Providing care and health services to people with diabetes provides useful information that can be used to identify, treat, follow-up care and even prevent diabetes. In this study, a new method is presented to improve the diagnosis and prevention of diabetes using data mining methods. In this research, the DBSCAN clustering algorithm is used to cluster the data. Then, using SVM, we classify the data to identify useful data, and finally, with the firefly algorithm, we increase the obtained data to increase we optimize performance with this algorithm. The results of this study indicate that the DBSCAN algorithm is more efficient than other clustering algorithms. Also, the SVM algorithm can achieve 98% accuracy, which compared to other data mining algorithms could achieve a higher accuracy percentage. [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.

The article presents a case study of a 45-year-old man presented to the emergency department with a two-to-three-week history of feeling unwell with nausea and vomiting. It informs that the initial diagnosis was ketoacidosis precipitated by a lower respiratory tract infection in a participant without known diabetes. It highlights that the patient was educated on diabetes and insulin administration and discharged on a basal bolus insulin regimen.

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.