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There are no obvious clinical signs of a silent disease until it causes irreversible damage. As computerized technologies become more common for early detection of ailments, severity can be minimized. In order to minimize the severity of ailments, computerized technologies are being used more and more commonly for early detection through internet of things (IoT) platforms for e-Health, sometimes called the "internet of medical things (IoMT)". Nevertheless, unpredictability, inapplicability, and instability were problems with previous automated decision-making models. In this study, we present a hybrid model that combines optimum learning and iterative neighborhood component analysis (iNCA) by relying on neighborhood component analysis and feature aggregation. The support vector machine (SVM) algorithm has shown promising results when classifying numerous diseases based on water cycle algorithms (WCA). By using the WCA method, it is possible to find effective parameters at the local and global levels. In addition, the features with the lowest error level are selected from the pool of features. Hence, we developed a procedure to improve diagnostic accuracy and avoid overfitting. Using the IoMT platform, we validated the method on diabetes, hepatitis, breast cancer, and dermatology data from the UCI database. Moreover, we compared the proposed strategy with the state-of-the-art and thus, our decision-making system performed better than similar methods in identifying silent diseases. The proposed approach combines a model-oriented hybrid design with IoMT platform to assign proper treatments to patients and be both clinically applicable and appropriate for computer-aided design. [ABSTRACT FROM AUTHOR]

Concerning decision support in specific disease areas, making faster and more accurate medical diagnoses has become more challenging. Due to indeterminate, inconsistent, and huge patient data, judging a specific disease may be challenging. Similarity is an effective method to enhance healthcare systems by simplifying medical diagnostic generation compared with illness profiles. This paper proposes a new medical diagnosis algorithm that uses interval type-2 fuzzy similarity measures (IT-2FSMs). This algorithm calculates the degree of similarity between varieties of components of patient data and establishes methods of clustering patients based on close distances between some of their features. The proposed approach will be evaluated on UCI Machine Learning Repository medical datasets. Comparative studies will be done between different IT-2 FSMs (Cherif, Jaccard, Bustince, Mitchell, Gorzalczany, Zeng and Li) to demonstrate the capability of IT-2FSMs to make quick medical diagnoses, even with different noise levels. The proposed IT-2FSMs are utilized as a clustering method and compared against existing clustering algorithms such as type-2 fuzzy c-means, cluster forest, bagged clustering, evidence accumulation, and random projection. The IT-2FSMs demonstrate a pertinent classification accuracy comparable to the other algorithms, as assessed by the clustering quality parameter R. [ABSTRACT FROM AUTHOR]

Linear Diophantine fuzzy soft set (LDFSS) is a powerful tool to handle uncertain information more flexibly and comprehensively. The aim of this paper is to propose some information measures (similarity measure, distance measure and entropy) for the LDFSS environment. For this aim, we first redefine the notion of LDFSS from a more general perspective. Then, we extend some well-known classical distances such as Hamming distance, Euclidean distance, Minkowski distance and etc. for the LDFSS environment. Also, we give some similarity measures where some of which are constructed from the presented distance measures and introduce the entropy measure for the LDFSSs. After, we construct two different decision-making methods, one based on the presented similarity measures and the other based on the TOPSIS method with entropy. Finally, we demonstrate a numerical example related to the medical diagnosis to illustrate that the proposed methods are more objective and feasible in the applications. [ABSTRACT FROM AUTHOR]

Gene expression profiling for cancer diagnosis requires the identification of optimal and non-redundant gene subsets from microarray data. We present a multi-objective particle swarm optimization (PSO) approach that balances gene-class relevancy and inter-gene redundancy by integrating mutual information. Our method employs a dual-phase search strategy: an initial PSO search followed by a local search to accelerate convergence, and a subsequent Pareto front selection to extract the non-dominated gene subsets. Experiments on cancer microarray benchmark datasets demonstrate that our approach significantly enhances feature selection and diagnosis accuracy compared to existing methods. Notably, our approach incorporates a novel dual-evaluation framework and an improved particle representation scheme, which collectively enhance robustness and prevent premature convergence. These innovations ensure a comprehensive and effective gene selection process for cancer diagnosis. [ABSTRACT FROM AUTHOR]

In artificial intelligence, decision-making is a widely studied field, and distance measure plays a crucial role in decision science. Several distance measures for Intuitionistic Fuzzy sets (IFSs) have been developed to handle uncertain situations. However, some of these measures lack properties of distance measures or produce unreasonable results. This study introduces a new distance measure between IFSs that satisfies the necessary properties and resolves counter-intuitive cases. The proposed distance measure is verified to be more reasonable through numerical examples, and it is applied to pattern recognition and medical diagnosis problems. [ABSTRACT FROM AUTHOR]

Magnetoelastic biosensors have emerged as a promising technology for the sensitive and label-free detection of a wide range of biological analytes. These biosensors use the magnetoelastic effect, which describes how the mechanical properties of magnetostrictive materials change in response to a magnetic field. This effect is utilized to detect biological analytes by immobilizing specific recognition elements, such as antibodies or nucleic acids, on the magnetoelastic material's surface. The binding of target analytes to the recognition elements induces a mass change, leading to a shift in the resonance frequency of the magnetoelastic material. Magnetoelastic biosensors find applications across various fields, including medical diagnostics, environmental monitoring, and food safety. In medical diagnostics, they offer rapid and sensitive capabilities for detecting pathogens, biomarkers, and toxins. For environmental monitoring, they demonstrate the ability to detect pollutants and heavy metals. Furthermore, in ensuring food safety and quality, magnetoelastic biosensors detect allergens, pathogens, and contaminants effectively. Ongoing research and technological advancements suggest that these biosensors hold great potential for revolutionizing various fields, including healthcare, environmental monitoring, and food safety, contributing to improved disease diagnosis, environmental protection, and public health. This review article provides an overview of the principles, fabrication methods, diagnosis, bacterial density, food, and agricultural applications of magnetoelastic biosensors. [ABSTRACT FROM AUTHOR]

Distance measure is a strong tool to find out the discriminant between two Pythagorean fuzzy sets ( P Y F S s ). Various types of distance measures exist under a Pythagorean fuzzy environment, but most of these measures have given absurd outcomes and considered the distance between the numerical values of Pythagorean fuzzy parameters. In this paper, we propose an Intuitive Pythagorean fuzzy ( P Y F ) distance measure, which satisfies the properties of conventional distance measure and highlights the properties of Pythagorean fuzzy information. After that, based on an extended artificial benchmark test set, an analysis on comparison between the proposed Intuitive measure and the existing measures is carried out, in which nine pairs of singleton P Y F S s are used. Pattern Detection and medical diagnosis are handled with the intuitive distance measure. In addition, a comparison is made between the outcomes of the intuitive distance measure and the current measures to demonstrate that the findings of the proposed measure's calculation yield the maximum level of confidence. Furthermore, we investigate the TOPSIS–VIKOR (Technique for Order Performance by Similarity to Ideal Solution—Vlekriterijumsko KOmpromisno Rangiranje) method, which is based on the proposed distance measure of P Y F S s . With the help of illustration, various methods for multi-attribute decision-making (MADM) issues are compared and analyzed to demonstrate the efficacy and feasibility of the TOPSIS–VIKOR method. [ABSTRACT FROM AUTHOR]

Lung disease is one of the leading causes of death worldwide. This emphasizes the need for early diagnosis in order to provide appropriate treatment and save lives. Physicians typically require information about patients' clinical symptoms, various laboratory and pathology tests, along with chest X-rays to confirm the diagnosis of lung disease. In this study, we present a transformer-based multimodal deep learning approach that incorporates imaging and clinical data for effective lung disease diagnosis on a new multimodal medical dataset. The proposed method employs a cross-attention transformer module to merge features from the heterogeneous modalities. Then unified fused features are used for disease classification. The experiments were performed and evaluated on several classification metrics to illustrate the performance of the proposed approach. The study's results revealed that the proposed method achieved an accuracy of 95% in terms of accurate classification of tuberculosis and outperformed other traditional fusion methods on multimodal tuberculosis data used in this study. [ABSTRACT FROM AUTHOR]

Machine learning and medical diagnostic studies often struggle with the issue of class imbalance in medical datasets, complicating accurate disease prediction and undermining diagnostic tools. Despite ongoing research efforts, specific characteristics of medical data frequently remain overlooked. This article comprehensively reviews advances in addressing imbalanced medical datasets over the past decade, offering a novel classification of approaches into preprocessing, learning levels, and combined techniques. We present a detailed evaluation of the medical datasets and metrics used, synthesizing the outcomes of previous research to reflect on the effectiveness of the methodologies despite methodological constraints. Our review identifies key research trends and offers speculative insights and research trajectories to enhance diagnostic performance. Additionally, we establish a consensus on best practices to mitigate persistent methodological issues, assisting the development of generalizable, reliable, and consistent results in medical diagnostics. [ABSTRACT FROM AUTHOR]

Nature inspires technological innovation through unique micronanostructured surfaces, such as fish fins, lotus leaves, butterfly wings, rose petals, a bird's spongy bone, etc. These structures exhibit capabilities beyond conventional engineering, making biomimetics a key focus of scientific research. Integrating nanotechnology, biology, and bioengineering has significantly propelled the development of nanomaterials with distinct functions and properties. This interdisciplinary synergy has extensively advanced biomimetic nanomaterials. This review highlights biomimetic nanomaterials, with an emphasis on state-of-the-art sensing devices. In addition to this, it furnishes an extensive compendium of investigations into synthesis and fabrication of biomimetic nanomaterials, such as metal–organic frameworks (MOFs), molecularly imprinted polymers (MIPs), carbon-based biomimetic nanomaterials (graphene, carbon nanotubes), and gold nanoparticles (AuNPs), for biosensor development. We have focused on their applications in disease diagnostics within healthcare while also addressing their role in environmental monitoring and agriculture. This comprehensive review aims to impart an insightful understanding of the scientific complexities associated with these technologies and concludes with an appraisal of current challenges and future developments. [ABSTRACT FROM AUTHOR]

Picture fuzzy sets (PFSs), as a generalization of traditional fuzzy sets and intuitionistic fuzzy sets (IFSs), offer a powerful framework for modeling and dealing with imprecise and uncertain information and have been widely used in various fields. Nevertheless, how to effectively measure the differences or similarities between PFSs is still a challenging and urgent problem. Despite that some distance measures for PFSs have been developed, some of them have counterintuitive and unreasonable results and even partially fail to satisfy the axiomatic definition of distance measures. To handle these limitations, in this paper, we propose two distance measures of PFSs motivated by the merits of Jensen–Shannon divergence. Specifically, the first distance measure of the PFSs considers the positive degree, neutral degree and negative degree. The second distance measure introduces the refusal degree on this basis. Moreover, the proposed distance measures not only satisfy the axiomatic definition but also overcome the counterintuitive results of existing distance measures. Finally, two kinds of applications related to pattern recognition and medical diagnosis are used to verify the performance of the proposed distance measures. [ABSTRACT FROM AUTHOR]

Internet technology and smart medical equipment have been rapidly developed and widely popularized. Telemedicine can improve the efficiency of patients' medical treatment and can facilitate patients to receive doctor's diagnosis and treatment, which saves medical resources and facilitate the monitoring of patients suffering from geriatric diseases and chronic diseases. In this work, we adopt the deep learning model to form a medical‐oriented English translation mechanism, based on which we can improve diagnostic efficiency and promote the development of smart telemedicine systems. We combine the recurrent neural network and attention mechanism to design an English translation model, which can solve the language barrier in the cross‐domain medical diagnosis process and improve the diagnosis efficiency. Specifically, first, we design a RNN translation model based on the encoder‐decoder structure. Second, we incorporate an attention mechanism into the designed translation model to improve the translation performance of the model. The experimental results show that the proposed mechanism is efficient. [ABSTRACT FROM AUTHOR]

The progress of machine learning has revealed its immense potential in emerging medical diagnosis application. This application enables medical users to access diagnosis service that utilizes machine learning models held by service providers. However, the protection of user data and models has emerged as a significant concern, leading to the proposal of numerous privacy-preserving medical diagnosis schemes. Unfortunately, many of these schemes rely on heavy cryptographic primitives like homomorphic encryption, resulting in lengthy diagnosis processes. To address this issue, we present a novel privacy-preserving medical diagnosis scheme that leverages Gaussian kernel-based support vector machine and employs the use of a lightweight primitive known as additive secret sharing. We design protocols for secure decision function computation and secure sign function computation, solving both two-class and multi-class medical diagnosis problems. Furthermore, our solution is generic and applicable to various scenarios that involve the Gaussian kernel-based support vector machine. To validate our scheme, we conduct evaluations on six real medical datasets. And the results demonstrate that our privacy-preserving medical diagnosis approach yields more accurate and efficient outcomes. Specifically, it achieves an accuracy of 99.09% on the dermatology dataset, surpassing the existing schemes using linear support vector machine. In addition, it takes 133ms on the breast cancer dataset, which is significantly faster than the schemes based on homomorphic encryption. [ABSTRACT FROM AUTHOR]

Abstract This paper introduces a novel distance measure for dual hesitant fuzzy sets (DHFS) and weighted dual hesitant fuzzy sets (WDHFS), with a rigorous proof of the triangular inequality to ensure its mathematical validity. The proposed measure extends the normalized Hamming, generalized, and Euclidean distance measures to dual hesitant fuzzy elements (DHFE), offering a broader framework for handling uncertainty in fuzzy environments. Additionally, the utilization of a score function is shown to simplify the computation of these distance measures. The practical relevance of the proposed measure is demonstrated through its application in medical diagnosis and decision-making processes. A comparative analysis between the newly introduced distance measure denoted as $$\chi$$ χ , and an existing measure, $$\chi _1$$ χ 1 is performed to underscore the superiority and potential advantages of the new approach in real-world scenarios.

Abstract Expert system recommendation assists the healthcare system to develop in real-time monitoring and diagnosis of patient conditions over several healthcare institutions. Privacy concerns, however, present significant problems since patient data leaks can lead to big effects including financial losses for hospitals and invasions of personal privacy for people. To address these issues, the research introduces a privacy-preserving collaborative medical diagnosis (CMD) method on a federated learning (FL). FL maintains patient privacy and data localization by spreading only model parameters, therefore enabling training models on remote datasets. The combination of Partially Homomorphic Cryptosystem (PHC) and Residual Learning based Deep Belief Network (RDBN) ensures an accurate and safe classification of patient physiological data. Experimental results show that the proposed method is successful in maintaining the diagnostic accuracy over numerous healthcare institutions and protecting privacy. The results show that the RDBN and PHC computations requires around 1000 ms and 150 ms, respectively for classification and privacy; the data transmission from the user to server and from server to user is 5 MB and 4 MB, respectively. Finally with a 30% reduction in overhead, the proposed approach offers an average increase in classification accuracy of 10% over multiple datasets.

A generalized hybrid distance measure of a single value neutrosophic set (SVNS) is proposed to analyze the patient's factors and diseases. Since Neutrosophic Set (NS) may communicate contradictory and ambiguous information, it is a crucial and useful tool for modelling uncertainty information. A distance measure for NS information is a key tool that is always employed in many approaches including medical diagnostics. Several distance measures in NS can be found in the literature, however, only a few of them proposed hybrid techniques. This paper aims to develop a new distance measure by considering exponential function and to define new hybrid distance measures for NS. The work also includes the formulation of the properties and application in the medical diagnosis for the validity phase. This study examines the possibility of cardiac problems in pregnant women. The eight factors considered in this study are age, obesity, smoking, family pathological history, personal pathological history, electrocardiogram, ultrasound, and functional class. The generalized hybrid distance measure has been used to discuss the factors of six different diseases, present consistent results. [ABSTRACT FROM AUTHOR]

This paper proposes an online medical pre-diagnosis scheme based on blockchain. It mainly focuses on the problems faced in the medical diagnosis scheme nowadays, such as the huge scale of medical treatment, the lack of data sharing, the impossibility of pursuing responsibility, etc., and constructs a safe and reliable online medical diagnosis scheme by using the characteristics of blockchain technology, such as traceability, decentralization, and openness and transparency. In this paper, the existing homomorphic encryption algorithm is first improved, and the algorithm homomorphism is utilized to generate a public key, which is able to diagnose the data in an encrypted state and ensure the security of the private key. When storing medical data, the original medical data are stored with the help of IPFS file system, and the hash value of medical records is chosen to be stored on the blockchain, which realizes the openness, transparency, and traceability of the data, which can effectively protect the privacy of the data, and also realize the effective pursuit of medical accidents. The specific security analysis, functional comparison, and performance analysis of the scheme further demonstrate that the scheme can safely and efficiently execute online medical pre-diagnosis programs. [ABSTRACT FROM AUTHOR]

The current study investigates the robustness of deep learning models for accurate medical diagnosis systems with a specific focus on their ability to maintain performance in the presence of adversarial or noisy inputs. We examine factors that may influence model reliability, including model complexity, training data quality, and hyperparameters; we also examine security concerns related to adversarial attacks that aim to deceive models along with privacy attacks that seek to extract sensitive information. Researchers have discussed various defenses to these attacks to enhance model robustness, such as adversarial training and input preprocessing, along with mechanisms like data augmentation and uncertainty estimation. Tools and packages that extend the reliability features of deep learning frameworks such as TensorFlow and PyTorch are also being explored and evaluated. Existing evaluation metrics for robustness are additionally being discussed and evaluated. This paper concludes by discussing limitations in the existing literature and possible future research directions to continue enhancing the status of this research topic, particularly in the medical domain, with the aim of ensuring that AI systems are trustworthy, reliable, and stable. [ABSTRACT FROM AUTHOR]

Traditional decision-making methods often struggle with inherent uncertainty and imprecision in data. Neutrosophic set-based algorithms offer a novel approach by incorporating degrees of truth, indeterminacy, and falsity. This paper explores the structure and implementation of these algorithms, highlighting their potential to handle real-world complexities. The application of neutrosophic sets in medical diagnosis is presented as a case study, demonstrating how the algorithm aids in evaluating probabilities for various diagnoses under uncertain conditions. The paper concludes by discussing limitations and potential applications in diverse fields. [ABSTRACT FROM AUTHOR]

The field of artificial intelligence (AI) and machine learning (ML) has been expanding and is explored by researchers in various fields. In medical diagnosis, for instance, the field of AI/ML is being explored because if medical diagnostic devices are built and designed with a backend of AI/ML, then the benefits would be unprecedented. Automated diagnostic tools would result in reduced health care costs, diagnosis without human intervention, overcoming human errors, and providing adequate and affordable medical care to a wider portion of the population with portions of the actual cost. One domain where AI/ML can make an immediate impact is medical imaging diagnosis (MID), namely the classification of medical images, where researchers have applied optimization techniques aiming to improve image classification accuracy. In this paper, we provide the research community with a comprehensive review of the most relevant studies to date on the use of deep CNN architecture optimization techniques for MID. As a case study, the application of these techniques to COVID-19 medical images were made. The impacts of the related variables, including datasets and AI/ML techniques, were investigated in detail. Additionally, the significant shortcomings and challenges of the techniques were touched upon. We concluded our work by affirming that the application of AI/ML techniques for MID will continue for many years to come, and the performance of the AI/ML classification techniques will continue to increase.

Mpox, a zoonotic viral disease, poses a significant threat to human health, characterized by its potential for human-to-human transmission and its manifestation in severe flu-like symptoms and distinctive skin lesions. This paper offers a comprehensive exploration of Mpox detection and classification, beginning with an introduction to the subject and a description of the research objectives and scope. A thorough examination of the historical context and epidemiology of Mpox sets the stage for a detailed discussion of the fundamental background concepts, encompassing medical imaging, various types of medical imaging techniques, machine learning (ML) applications, convolutional neural networks (CNNs), and available architectural families. The study highlights essential model evaluation metrics to provide a robust framework for assessing the efficacy of different approaches. Methodologically, the paper outlines the systematic approach employed in the literature review and study selection process. With an emphasis on benchmark datasets, the research delves into the diverse AI-based methodologies, encompassing both ML and deep learning (DL) approaches, utilized in Mpox detection. The paper meticulously describes the challenges inherent in these methodologies and concludes with a thoughtful exploration of future prospects in the field. The main purpose is to provide a comprehensive overview of the current landscape and pave the way for advancements that can significantly impact the diagnosis and management of Mpox outbreaks. [ABSTRACT FROM AUTHOR]

Early skin cancer diagnosis saves lives as the disease can be successfully treated through complete excision. Computer-aided diagnosis methods are developed using artificial intelligence techniques to help earlier detection and identify hidden causes leading to cancers in skin lesion images. In skin cancer image classification problems, an ensemble of classifiers has demonstrated better classification ability than a single classification algorithm. Traditionally, training an ensemble uses the complete set of original features, where some of these features can be redundant or irrelevant and hence, may not provide useful information in generating good models for ensemble classification. Moreover, newly created features may help improve classification performance. To address this issue, the existing methods have used feature construction for building an ensemble classifier, which usually creates a fixed number of features that may fit the training data too well, resulting in poor test performance. This study develops a novel classification approach that combines ensemble learning, feature selection, and feature construction utilizing genetic programming (GP) to handle the above limitations. The proposed method automatically evolves variable-length feature vectors consisting of GP-selected and GP-constructed features suitable for training an ensemble classifier. This study evaluates the effectiveness of the proposed method on two benchmark real-world skin image datasets that include dermoscopy and standard camera images. The experimental results reveal that the proposed algorithm significantly outperforms four state-of-the-art convolutional neural network methods, the existing GP approaches, and 11 commonly used machine learning methods. Furthermore, this study also includes interpreting evolved individuals that highlight important skin cancer characteristics playing a vital role in discriminating images of different cancer classes. This study shows that high classification performance can be achieved at a low cost of computational resources and inference time, and accordingly, this method is potentially suitable to be implemented in mobile devices for the automated screening of skin lesions and many other malignancies in low-resource settings. [ABSTRACT FROM AUTHOR]

Medical deep neural networks (DNNs) trained upon coarse image inputs are inherently insensible to fine-grained anatomic features. To enhance DNN perception on delicate microvascular structures, we proposed using a straightforward angiographic mobile-dense hybrid network (AMDenseNet) in tandem with a flexible input split, suppression, and swap perturbation (InSSS-P) framework to perform explainable diagnostics for microvascular diseases. Mechanistically, InSSS-P and AMDenseNet conjointly (1) decompose complex anatomy inputs into LEGO-like blocks, then (2) distill plexus-wise vascular block significance from the rearranged anatomy input-output samplings. To validate the robustness of the proposed model, we trained AMDenseNet to detect blind-threatening retina exudative age-related macular degeneration (exAMD) from the micrometer-scaled optic coherence tomographic angiography (OCTA), wherein our AMDenseNet annotated triple-specific (plexus, branch, and exudate activity) neovascular (NV) features at a diagnostic precision that is comparable to the gold standard dye-based angiography. Interestingly, when we applied plexus-wise InSSS-P to the AMDenseNet predictions, the permutate results demonstrated that input perturbation at deep capillary plexus (DCP) layer abrogated model-awared exAMD features and resulted in false-negative exAMD impressions. On the contrary, choroid capillary (CC) input perturbations contributed to false-positive exAMD characterization. Notably, the AMDenseNet triple-specific NV annotation functionality was disrupted by either DCP or CC input perturbations, indicating the significance of DCP and CC in the task of exAMD characterization. In sum, this study employed anatomy decomposition approaches to distill plexus-wise microvascular significance and highlighted the analytic potential of leveraging an anatomy-sensible model to discover novel disease biomarkers. [ABSTRACT FROM AUTHOR]

The field of artificial intelligence (AI) and machine learning (ML) has been expanding and is explored by researchers in various fields. In medical diagnosis, for instance, the field of AI/ML is being explored because if medical diagnostic devices are built and designed with a backend of AI/ML, then the benefits would be unprecedented. Automated diagnostic tools would result in reduced health care costs, diagnosis without human intervention, overcoming human errors, and providing adequate and affordable medical care to a wider portion of the population with portions of the actual cost. One domain where AI/ML can make an immediate impact is medical imaging diagnosis (MID), namely the classification of medical images, where researchers have applied optimization techniques aiming to improve image classification accuracy. In this paper, we provide the research community with a comprehensive review of the most relevant studies to date on the use of deep CNN architecture optimization techniques for MID. As a case study, the application of these techniques to COVID-19 medical images were made. The impacts of the related variables, including datasets and AI/ML techniques, were investigated in detail. Additionally, the significant shortcomings and challenges of the techniques were touched upon. We concluded our work by affirming that the application of AI/ML techniques for MID will continue for many years to come, and the performance of the AI/ML classification techniques will continue to increase. [ABSTRACT FROM AUTHOR]

Spherical q-linear Diophantine fuzzy sets (Sq-LDFSs) proved more effective for handling uncertainty and vagueness in multi-criteria decision-making (MADM). It does not only cover the data in two variable parameters but is also beneficial for three parametric data. By Pythagorean fuzzy sets, the difference is calculated only between two parameters (membership and non-membership). According to human thoughts, fuzzy data can be found in three parameters (membership uncertainty, and non-membership). So, to make a compromise decision, comparing Sq-LDFSs is essential. Existing measures of different fuzzy sets do, however, can have several flaws that can lead to counterintuitive results. For instance, they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change, even though each parameter has a different implication. In the Sq-LDFSs comparison, this research develops the differential measure (DFM). The main goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally. Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees, two Sq-LDFSs form this preference connection when the uncertainty remains same in both sets. According to the degree of superiority or inferiority, two Sq-LDFSs are shown as identical, equivalent, superior, or inferior over one another. The suggested DFM's fundamental characteristics are provided. Based on the newly developed DFM, a unique approach to multiple criterion group decision-making is offered. Our suggested method verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs. Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero. The method's applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches. To assess its stability and usefulness, a sensitivity analysis is done. [ABSTRACT FROM AUTHOR]

The detection of biomarkers (such as DNA, RNA, and protein) plays a vital role in medical diagnosis. The CRISPR-based biosensors utilize the CRISPR/Cas system for biometric recognition of targets and use biosensor strategy to read out biological signals without the employment of professional operations. Consequently, the CRISPR-based biosensors demonstrate great potential for the detection of biomarkers with high sensitivity and specificity. However, the signal readout still relies on specialized detectors, limiting its application in on-site detection for medical diagnosis. In this review, we summarize the principles and advances of the CRISPR-based biosensors with a focus on medical diagnosis. Then, we review the advantages and progress of CRISPR-based naked eye biosensors, which can realize diagnosis without additional detectors for signal readout. Finally, we discuss the challenges and further prospects for the development of CRISPR-based biosensors. [ABSTRACT FROM AUTHOR]

Community-acquired pneumonia is one of the most lethal infectious diseases, especially for infants and the elderly. Given the variety of causative agents, the accurate early detection of pneumonia is an active research area. To the best of our knowledge, scoping reviews on diagnostic techniques for pneumonia are lacking. In this scoping review, three major electronic databases were searched and the resulting research was screened. We categorized these diagnostic techniques into four classes (i.e., lab-based methods, imaging-based techniques, acoustic-based techniques, and physiological-measurement-based techniques) and summarized their recent applications. Major research has been skewed towards imaging-based techniques, especially after COVID-19. Currently, chest X-rays and blood tests are the most common tools in the clinical setting to establish a diagnosis; however, there is a need to look for safe, non-invasive, and more rapid techniques for diagnosis. Recently, some non-invasive techniques based on wearable sensors achieved reasonable diagnostic accuracy that could open a new chapter for future applications. Consequently, further research and technology development are still needed for pneumonia diagnosis using non-invasive physiological parameters to attain a better point of care for pneumonia patients. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis of utilizing TensorFlow Lite on mobile phones for the on-edge medical diagnosis of lung diseases. This paper focuses on the technical deployment of various deep learning architectures to classify nine respiratory system diseases using X-ray imagery. We propose a simple deep learning architecture that experiments with six different convolutional neural networks. Various quantization techniques are employed to convert the classification models into TensorFlow Lite, including post-classification quantization with floating point 16 bit representation, integer quantization with representative data, and quantization-aware training. This results in a total of 18 models suitable for on-edge deployment for the classification of lung diseases. We then examine the generated models in terms of model size reduction, accuracy, and inference time. Our findings indicate that the quantization-aware training approach demonstrates superior optimization results, achieving an average model size reduction of 75.59%. Among many CNNs, MobileNetV2 exhibited the highest performance-to-size ratio, with an average accuracy loss of 4.1% across all models using the quantization-aware training approach. In terms of inference time, TensorFlow Lite with integer quantization emerged as the most efficient technique, with an average improvement of 1.4 s over other conversion approaches. Our best model, which used EfficientNetB2, achieved an F1-Score of approximately 98.58%, surpassing state-of-the-art performance on the X-ray lung diseases dataset in terms of accuracy, specificity, and sensitivity. The model experienced an F1 loss of around 1% using quantization-aware optimization. The study culminated in the development of a consumer-ready app, with TensorFlow Lite models tailored to mobile devices. [ABSTRACT FROM AUTHOR]

The medical diagnosis of many critical diseases is difficult as it usually requires the combined effort of several doctors. At this time, the process of medical diagnosis is actually a group decision-making (GDM) problem. In group medical diagnosis, considering doctors' weight information and fusing the interaction relation of symptoms remain open issues. To address this problem, a group decision-making method for intuitionistic fuzzy soft environments is proposed for medical diagnosis because the intuitionistic fuzzy soft set (IFSS) integrates the advantages of the soft set and intuitionistic fuzzy set (IFS). Intuitionistic fuzzy soft weighted Muirhead mean operators are constructed by combining Einstein operations with the Muirhead mean (MM) operator, and some properties and results are revealed. A group medical diagnosis model with unknown doctor weight information and incomplete intuitionistic fuzzy soft information is proposed. Similarity measures of the intuitionistic fuzzy soft matrix (IFSM) given by the doctors are used to estimate the incomplete information. To take into account the advantages of objective weight and subjective weight, the combined weights of doctors are calculated based on the IFSMs' similarity measure and doctors' grades. The developed operators are then used to combine the evaluation information and handle the correlation of input arguments in the group medical diagnosis process. Finally, a numerical problem is selected to illustrate the superiority of the proposed approach compared to related methods. The combined weights are determined to overcome the shortcomings of the single-weight method to some extent. Meanwhile, the proposed method is more comprehensive, and can provide more flexible and reasonable choices for group medical diagnosis problems. [ABSTRACT FROM AUTHOR]

As highlighted in Dawid (2000) and Pearl & Mackenzie (2018) , deducing the causes of given effects is a more challenging problem than evaluating the effects of causes in causal inference. Lu et al. (2023) proposed an approach for deducing causes of a single effect variable based on posterior causal effects. In many applications, there are multiple effect variables, and they can be used simultaneously to more accurately deduce the causes. To retrospectively deduce causes from multiple effects, we propose multivariate posterior total, intervention and direct causal effects conditional on the observed evidence. We describe the assumptions of no confounding and monotonicity, under which we prove identifiability of the multivariate posterior causal effects and provide their identification equations. The proposed approach can be applied for causal attributions, medical diagnosis, blame and responsibility in various studies with multiple effect or outcome variables. Two examples are used to illustrate the proposed approach. [ABSTRACT FROM AUTHOR]

In the realm of medical diagnosis, intuitionistic fuzzy data serves as a valuable tool for representing information that is uncertain and imprecise. Nevertheless, decision-making based on this kind of knowledge can be quite challenging due to the inherent vagueness of the data. To address this issue, we employ power aggregation operators, which prove effective in combining several sources of data, such as expert thoughts and patient information. This allows for a more correct diagnosis; a particularly crucial aspect of medical practice where precise and timely diagnoses can significantly impact medication policy and patient results. In our research, we introduce a novel methodology to the three-way decision idea. Initially, we revamp the three-way decision model using rough set theory and incorporate interval-valued classes to handle intuitionistic fuzzy data. Secondly, we explore the use of intuitionistic fuzzy power weighted and intuitionistic fuzzy power weighted geometric aggregation operators to consolidate attribute values within the data system. Furthermore, we present a case study in the medical field to exhibit the validity and efficiency of our offered technique. This innovative method enables us to classify participants into three distinct zones based on their symptoms. The manuscript concludes with a summary of key points provided by the authors. [ABSTRACT FROM AUTHOR]

To create novel treatments and treat complex diseases, the pharmaceutical sector is essential. Drug discovery, however, is a time-consuming, pricey, and dangerous endeavor. Artificial intelligence (AI) has become a potent instrument that has transformed several industries, including healthcare, in recent years. This summary gives a general overview of how AI is expediting the creation of novel medicines, revolutionizing the pharmaceutical sector, and enabling drug discovery. The pharmaceutical sector is experiencing a drug discovery revolution because of AI. The drug discovery process is changing at different phases because of AI approaches like machine learning and deep learning. This abstract demonstrates how AI facilitates drug development through target identification, lead compound optimization, drug design, drug repurposing, and clinical trial enhancement. AI integration has the potential to hasten the creation of novel treatments, save costs, and improve patient outcomes. To fully realize the potential of AI in pharmaceutical research and development, issues relating to data accessibility, algorithm interpretability, and laws must be resolved. [ABSTRACT FROM AUTHOR]

The feature selection task is a crucial phase in data analysis, aiming to identify a minimized set of relevant features for the target class, thereby eliminating irrelevant and redundant attributes used for model training. While population-based feature selection approaches offer prominent solutions for classification performance, their computational time can be prohibitive. To mitigate delays and optimize resource utilization, this study adopts machine learning operations (MLOps). MLOps involves the seamless transition of experimental machine learning models into production, serving them to end users and automating the feature selection phase. This paper introduces a novel feature selection method based on improved migrating bird optimization and its automated variant integrated into MLOps. Experiments conducted on six medical datasets validate the effectiveness of our proposed feature selection method in improving the outcomes of medical diagnosis systems. The results showcase satisfactory performance in terms of classification compared to concurrent feature selection algorithms. [ABSTRACT FROM AUTHOR]

Uncertainty in medical diagnosis is the main challenge in medical emergencies (MEs) experienced by triage nurses and physicians in the emergency department (ED). The intuitionistic fuzzy correlation coefficient (IFCC) approach is used to analyze and interpret the relationship between variables in an uncertain environment. Assorted methods that involve applying a correlation coefficient under intuitionistic fuzzy sets (IFSs) were constructed based on Pearson's correlation model with various drawbacks. In this work, we construct two new intuitionistic fuzzy correlation measures (IFCMs) based on Spearman's correlation model. It is demonstrated that the Spearman-based IFCMs are appropriate for measuring correlation coefficients without any drawbacks. In addition, we show that the Spearman-based IFCMs overcome all the shortcomings of the associated IFCC methods. Equally, the Spearman-based IFCMs satisfy the maxims of the correlation coefficient that have been delineated in the classical case of correlation coefficient. Due to the challenges that uncertainty in medical diagnosis pose to MEs and the proficiency of the IFCC approach, we discuss the application of the constructed IFCMs in a triage process for an effective medical diagnosis during an ME. The medical data for the triage process are obtained via a knowledge-based approach. Finally, comparative analyses are carried out to ascertain the validity and authenticity of the developed Spearman-based IFCMs relative to other IFCC approaches. [ABSTRACT FROM AUTHOR]

This paper examines how participants in genetic counseling sessions interactionally manage situations where the results of tests to investigate the causes of identified fetal malformations are inconclusive or missing. The dataset consists of 54 audio-recorded interactions at a unit specialized in moderate- and high-risk pregnancies at a Brazilian public hospital. Conversation analysis was used to examine the data, revealing that the participants deployed interactional actions that exhibited highly negative valence toward diagnostic inconclusiveness, demonstrating that when there is a motivation for a medical examination, insofar as its results will serve as a basis for subsequent decision-making (in this case about future pregnancies), there is a preference for bad diagnostic news over absent or inconclusive diagnostic news. These findings are consistent with prior interactional studies.