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This paper provides a literature review addressing the use of soft sets in medical diagnosis. Distinguishing itself from the existing literature, the study offers a comprehensive analysis of how fuzzy soft sets can be integrated into diagnostic processes, highlighting a novel fusion of fuzzy and soft sets in medical applications. Any soft set on a countably infinite universe can be regarded as a fuzzy set, recognizing the limitations of traditional diagnostic tools in dealing with vague and incomplete information, our research aims to utilize the flexibility and comprehensiveness of fuzzy soft sets to enhance decision-making accuracy in medical scenarios. The primary objective of this research is to present a thorough and critical analysis of fuzzy soft set theory in medical diagnosis, aiming to establish it as a fundamental approach in the field. By combining all of these results, we can generate a comprehensive picture of the connections between the many theories that account for fuzziness and imprecision, which helps to fill in the blanks left by recent surveys. The review will focus on identifying the main research trends in this field: the primary research topics that soft set theory addresses in medical applications, the community is currently facing and the major theoretical concepts used to study these topics. The primary objective of this review is to assist in the identifying of emerging research directions. Some of these trends are promising and will help shape a new role for soft set theory in the area of medical applications. The fusion of fuzzy and soft sets in medical applications represents a crucial and necessary stage in the research and diagnosis procedures. Key results include the study on development of algorithms and models that outperform traditional methods in accuracy and reliability.

Volatile organic compounds (VOCs) are important biomarkers in exhaled breath or skin secretion of patients under various medical or pre-medical conditions. As such, VOCs have been explored as alternative biomarkers in the detection of diseases, including asthma, tuberculosis, and lung cancer. In this regard, a rapid, cost-effective, facile, and sample-free strategy is advantageous and critically needed for premedical screening, medical diagnosis, and health monitoring. In this review, we present an overview of the latest progress of using nanomaterial-based chemo-resistive VOC sensors for fast, real-time, and non-invasive diagnosis of diseases via detecting VOCs from exhaled breath and other sources from human body. The origin and emission of VOCs are summarized from human body, and the VOC signatures are discussed as related to specific disease. Targeting specific VOCs, chemoresistive sensors using different nanomaterials are reviewed in terms of their sensing performance metrics including sensitivity, selectivity, response/recovery time, and stability. Various strategies for improving VOC sensor performance are discussed, specifically on the material and signal processing-based approaches.

Medical reasoning describes a form of qualitative inquiry that examines the cognitive (thought) processes involved in making medical decision. In this field the goal for diagnostic reasoning is assessing causes of observed conditions in order to make informed choices about treatment. In order to design a diagnostic reasoning method we merge ideas from a hypothetic-deductive method and the Domino model. In this setting, we introduce the so called Hypothetic-Deductive-Domino (HD-D) algorithm. In addition, a multi-agent approach is presented, which takes advantage of the HD-D algorithm for illuminating different standpoints in a diagnostic reasoning and assessment process, and for reaching a well-founded conclusion. This multi-agent approach is based on the so called Observer and Validating agents. The Observer agents are supported by a deductive inference process and the Validating agents are supported by an abductive inference process. The knowledge bases of these agents are captured by a class of possibilistic logic programs. Hence, these agents are able to deal with qualitative information. The approach is illustrated by a real scenario from diagnosing dementia diseases.

Medical diagnosis is mostly done by experienced doctors. However, still some of the cases reported of wrong diagnosis and treatment. Patients are needed to take number of clinical tests for disease diagnosis. Most of the cases, all the tests are not contributing towards efficient diagnosis. The medical data are multidimensional and composed of thousands of independent features. So, the multidimensional database need to be analyzed and preprocessed for valuable decision making for medical diagnosis. The aim of this work is to accurately predict the medical disease with a condensed number of attributes. In this approach, the raw input dataset is preprocessed based on the common normalization approach. An association rule is used to find out the frequent used patterns to prune the dataset. Further, base rule can be applied to the pruned dataset. The Payoff and Heuristic rate can be evaluated to predict the risk analysis. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) approaches are used for better feature selection. Classification result is acquired based on minimum and maximum of residual support values. The experimental results show that the proposed scheme, can perform better than the existing algorithms to diagnose the medical disease.

Vagueness in scientific studies poses a challenge. Intuitionistic Fuzzy Set (IFS) theory has emerged as a powerful and flexible tool to counter such challenge. So, there is a need to develop such measures which can not only measure the vagueness but also quantify the differences in underlying IFSs. The aim of this communication is to introduce one such divergence measure called Intuitionistic Fuzzy Jensen-Tsalli Divergence measure in the settings of IFS theory. The presence of parameters makes the proposed divergence measure flexible and competent for applications. Besides discussing some of its major properties, the findings are applied in pattern recognition problem and in medical diagnosis of some diseases with same set of symptoms. The performance of the proposed measure is genuinely compared with some other existing measures in literature through numerical examples based on medical diagnosis and pattern recognition.

Esophagitis is essentially inflammation of the esophageal squamous mucosa. One of the major reasons for cause of Esophagitis is the acid reflux from the stomach. This condition is observed in the process of upper gastro-intestinal tract endoscopy and the diagnosis is arrived at by examining the images of the esophagus. The diagnosis is based on the observation of the lesions and coloration of the digestive mucosa. Our paper reports an implementation of Decision Support System (DSS) for diagnosis of Esophagitis based on the analysis of color and texture features of the images captured during the process of endoscopy. The Hue Saturation and Intensity color model is adapted. The statistical features of the Hue and Saturation form the color features and the texture features are determined by Discrete Cosine Transform coefficients of the image. The decision making structure is a feed forward neural network. The DSS has been tested and results are reported.

In a previous paper, we highlighted the design requirements of a computer-based system for the automated assessment of neuropsychological drawing tasks. In this paper, we shall examine the implementation of an analysis system specifically with reference to the software engineering principles utilized and the modular framework within with a flexible implementation can be realized. We shall highlight some of the implemented modules and, using two actual test batteries as examples, demonstrate the flow of information between each module. We shall also show the additional reporting and analysis features implemented for clinician support and describe how the framework can be utilized for more generic applications of handwriting/drawing analysis.

The concepts of relations and information measures have importance whenever we deal with medical diagnosis problems. The aim of this paper is to investigate the global pandemic COVID-19 scenario using relations and information measures in an interval-valued T-spherical fuzzy (IVTSF) environment. An IVTSF set (IVTSFS) allows describing four aspects of human opinions i.e., membership, abstinence, non-membership, and refusal grade that process information in a significant way and reduce information loss. We propose similarity measures and relations in the IVTSF environment and investigate their properties. Both information measures and relations are applied in a medical diagnosis problem keeping in view the global pandemic COVID-19. How to determine the diagnosis based on symptoms of a patient using similarity measures and relations is discussed. Finally, the advantages of dealing with such problems using the IVTSF framework are demonstrated with examples.

In the chronic disease diagnosis with high-dimensional clinical features, feature selection (FS) algorithms are widely applied to avoid sparse data. In current FS algorithms, only population features, which are in strong relevance with states of all patients, are extracted, while subspace features, which are in weak relevance with states of all patients but in strong relevance with states of patients under a certain state, are ignored. Eliminated relevant information in subspace features worsens the performance of current classification models. To alleviate the conflict of feature extraction in sparse data, we propose a two-phase classification model with relevant information in both population and subspace features considered. For a patient, his probability under each state is estimated in a space whose dimensions are population features in Phase 1, and in a space whose dimensions are subspace features under that state in Phase 2. The final result of the classification model is based on results in both phases. With both population and subspace features considered and probabilities under each state estimated in a low-dimensional space, the two-phase classification model outperforms other benchmark models both in accuracy and mean absolute error in the hepatic fibrosis diagnosis for patients with chronic hepatitis B.

Technology of brain–computer interface (BCI) provides a new way of communication and control without language or physical action. Brain signal tracking and positioning is the basis of BCI research, while brain modeling affects the treatment analysis of (EEG) and functional magnetic resonance imaging (fMRI) directly. This paper proposes human ellipsoid brain modeling method. Then, we use non-parametric spectral estimation method of time–frequency analysis to deal with simulation and real EEG of epilepsy patients, which utilizes both the high spatial and the high time resolution to improve the doctor’s diagnostic efficiency.

Gaze tracking has drawn increasing attention and applied wildly in the areas of disabled aids, medical diagnosis, etc. In this paper, a remote gaze tracking system is proposed. The system is video-based, and the video is captured under the illumination of near infrared light sources. Only one camera is employed in the system, which keeps the equipment portable for the users. The corneal glints and the pupil center, whose extraction accuracy determines the performance of the gaze tracking system, are obtained according to the gray distribution of the video frame. And then, the positions of the points on the screen that the user fixating are estimated by the gaze tracking algorithm based on cross-ratio-invariant. Additionally, a calibration procedure is necessary to eliminate the error produced by the deviation of the optical and visual axes. The proposed remote gaze tracking system has a low computational complexity and high robustness, and experiment results indicate that it is tolerant of head movement and still works well for users wearing glasses as well. Besides, the angle error of the gaze tracking system is 0.40 degree of the subjects without glasses, correspondingly, 0.48 degree of the subjects with glasses, which is comparable to most of the existing commercial systems and promising for most of the potential practical applications.

In medical diagnosis based on intuitionistic fuzzy sets (IFS), it is a general method to use max–min composition based on relations between the symptoms and diseases. The method, however, has been known to lead to quite conservative results and to a loss of information because the composition neglects most values except for extreme ones. To complement the shortcomings of the max–min composition, other measurements such as similarity and distance between IFS have gained attention as an important content in fuzzy mathematics from researchers. However, the methods based on the similarity and distance also have some drawbacks in that they provide an unclear diagnosis. For example, the distances for each disease do not have generally the same distribution. Therefore, there is a difference between the distance 0.25 for disease A and that for disease B. In addition, the difference in the distances for each disease would be very small. It can therefore be inferred that there would be no definite diagnostic criteria. To solve the problems, we propose a new approach for medical diagnosis based on Hamming distances in this study. In the approach, we do not use directly the Hamming distances but the distributional characteristics of the distances such as quantiles and p-values as a measure for diagnosis. To explore the potential for utilization of the approach, we present the simulation results. The simulation was applied to differentiate patients according to the three main types of primary headaches: migraine, tension and cluster headache. The result of the simulation indicates that it is possible to classify headache using the proposed method.

Volatile organic compounds (VOCs) are important biomarkers in exhaled breath or skin secretion of patients under various medical or pre-medical conditions. As such, VOCs have been explored as alternative biomarkers in the detection of diseases, including asthma, tuberculosis, and lung cancer. In this regard, a rapid, cost-effective, facile, and sample-free strategy is advantageous and critically needed for premedical screening, medical diagnosis, and health monitoring. In this review, we present an overview of the latest progress of using nanomaterial-based chemo-resistive VOC sensors for fast, real-time, and non-invasive diagnosis of diseases via detecting VOCs from exhaled breath and other sources from human body. The origin and emission of VOCs are summarized from human body, and the VOC signatures are discussed as related to specific disease. Targeting specific VOCs, chemoresistive sensors using different nanomaterials are reviewed in terms of their sensing performance metrics including sensitivity, selectivity, response/recovery time, and stability. Various strategies for improving VOC sensor performance are discussed, specifically on the material and signal processing-based approaches.

This paper introduces an idea, how the so-called excluding symptoms could be incorporated into the fuzzy relational compositions and provides an overview of valid properties.

A Multiple Objective Programming Method for an Intuitionistic Trapezoidal Fuzzy Model (MOPM-ITFM) is proposed to improve the accuracy of estimation by optimizing the parameters. The advantage of the proposal is more realistic to consider the degrees of both the acceptance and the rejection of intuitionistic trapezoidal fuzzy number (ITFN) than conventional fuzzy model only considering the former. The proposal is realized by tuning the membership function shapes, the adaptive factors of matching degree and the rule weights. The constraint condition of the proposal is considered in the definition of ITFN. Input fuzzy partitions of an Intuitionistic Trapezoidal Fuzzy Model (ITFM) are modified through the proposal. MOPM-ITFM has been used for a medical diagnosis. Compared with Single Objective Programming Method (SOPM), the results show that the mean square error (MSE) between the output given by domain experts and the output of (MOPM-ITFM) improves 47% than that of SOPM. In the further work, the interpretability of input fuzzy partitions is also needed to be improved in ITFM.

Cognitive feedback is a technique used for qualitative learning that has proven to be useful to train medical students. In this work we report the application of genetic algorithms to simulate this technique, using a knowledge-based system as the learner, in the domain of coronary artery disease diagnosis. The prototypical description of the disease employs fuzzy variables, as well as crisp ones. To evaluate the performance of the system, a similarity-triggered inference method is used over a diagnosed case-base. Results presented showing the efficiency of this approach, lead us to believe that this paradigm is useful for a wide range of applications.