Medical Diagnosis Problems Research Articles

Medical Diagnosis Problems Based on Neutrosophic Sets and Their Hybrid Structures: A Survey

Medical Diagnosis Problems Based on Neutrosophic Sets and Their Hybrid Structures: A Survey

Methods for Detecting COVID-19 Patients Using Interval-Valued T-Spherical Fuzzy Relations and Information Measures

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.

A q-rung orthopair basic probability assignment and its application in medical diagnosis

Dempster-Shafer theory is widely used in decision-making and considered as one of the potential mathematical tools in order to fuse the evidence. However, existing studies in this theory show disadvantage due to conflicting nature of standard evidence set and the combination rule of evidence. In this paper, we have constructed the framework of q-rung evidence set to address the issue of conflicts based on the q-rung fuzzy number due to its more comprehensive range of advantage compared to the other fuzzy or discrete numbers. The proposed q-rung evidence set has the flexibility in assessing a parameter through the q-rung orthopair basic probability assignment consisting of membership and non-membership belief degree. Moreover, as the proposed q-rung orthopair basic probability assignment consists of pair of belief degrees, the possibility of conflicts cannot be ignored entirely. In this regard, a new association coefficient measure is introduced where each component of the belief degrees is modified through the weighted average mass technique. This paper uses various concept such as fuzzy soft sets, Deng entropy, association coefficient measure and score function for decision-making problem. Firstly, to obtain the initial q-rung belief function, we have implemented the Intuitionistic fuzzy soft set to assess the parameter of the alternatives and Deng entropy to find the uncertainty of the parameters. Secondly, the association coefficient measure is used to avoid the conflict through the modified form of evidence. Finally, we combined the evidence and found the score value of the Intuitionistic fuzzy numbers for the ranking of the alternatives based on the score values of alternatives. This study is validated with the case study in the medical diagnosis problem from the existing paper and compared the ranking of alternatives based on the score function of belief measures of the alternatives.

The Sequence of Neutrosophic Soft Sets and a Decision-Making Problem in Medical Diagnosis

The Sequence of Neutrosophic Soft Sets and a Decision-Making Problem in Medical Diagnosis

Complex belief interval‐based distance measure with its application in pattern recognition

The complex evidence theory is an effective methodology for multiattribute decision-making. Since difference measure between multiattribute plays an important role for conflict management in the process of multiattribute decision-making, how to measure discrepancy between complex basic belief assignments (CBBAs) in complex evidence theory is still an open issue. In this context, a new distance measurement (complex belief distance—CBD) is proposed in this paper by taking advantages of complex belief function and complex plausibility function, called complex belief interval-based distance. In addition, we compare the proposed CBD with the related work to illustrate its superiority. Next, based on CBD, we devise a novel multiattribute decision-making algorithm for pattern recognition. Finally, we apply the method to problems of medical diagnosis to verify the effectiveness of the proposed method.

Cosine and Cotangent Similarity Measures Based on Choquet Integral for Spherical Fuzzy Sets and Applications to Pattern Recognition

The notion of trigonometric similarity measure (SM) for spherical fuzzy sets (SFSs) has become very important in solving various problems in pattern recognition and medical diagnosis. This study proposes some trigonometric SMs with the help of Choquet integral for SFSs. The proposed trigonometric SMs clearly satisfy the axiomatic definition of classical SMs. We also perform these SMs in pattern recognition problems to examine a comparative analysis of the proposed trigonometric SMs with some existing SMs. Spearman’s rank correlation coefficient is utilized to demonstrate the effectiveness of the proposed SMs.

Special issue: informatics & data-driven medicine-2021.

Modern medical diagnosis, treatment, or rehabilitation problems of the patient reach completely different levels due to the rapid development of artificial intelligence tools. Methods of machine learning and optimization based on the intersection of historical data of various volumes provide significant support to physicians in the form of accurate and fast solutions of automated diagnostic systems. It significantly improves the quality of medical services. This special issue deals with the problems of medical diagnosis and prognosis in the case of short datasets. The problem is not new, but existing machine learning methods do not always demonstrate the adequacy of prediction or classification models, especially in the case of limited data to implement the training procedures. That is why the improvement of existing and development of new artificial intelligence tools that will be able to solve it effectively is an urgent task. The special issue contains the latest achievements in medical diagnostics based on the processing of small numerical and image-based datasets. Described methods have a strong theoretical basis, and numerous experimental studies confirm the high efficiency of their application in various applied fields of Medicine.

Classification of Diabetes Types using Machine Learning

Machine learning algorithms have aided health workers (including doctors) in the processing, analysis, and diagnosis of medical problems, as well as the detection of disease patterns and other patient data. Diabetes mellitus (DM), commonly referred to as diabetes, is a gathering of a syndrome issue that is portrayed by high glucose levels in the blood over a drawn-out period. It is a long-term illness that is a great threat to humanity and causes death. Most of the existing machine learning algorithms used for the classification and prediction of diabetes suffer from embodying redundant or inessential medical procedures that cause complications and wastage of time and resources. The absence of a correct diagnosis scheme, deficiency of economic means, and a general lack of awareness represent the main reasons for these negative effects. Hence, preventing the sickness altogether through early detection may doubtless cut back a considerable burden on the economy and aid the patient in diabetes management. This study developed diabetes classification using machine learning techniques that will minimize the aforementioned drawbacks in the prediction of diabetes systems. Decision tree classifiers, logistic regression, random forest, and support vector machines are all examples of predictive algorithms that were tested in this paper. 1009 records of data set were obtained from the Diabetes dataset of Abelvikas, Data World. We used a confusion matrix to visualize the performance evaluation of the classifiers. The experimental result shows that the four machine learning algorithms perform well. However, Random Forest outperforms the other three, with a prediction accuracy of 100% and has a better prediction level when compared with others and existing work.

Introduction to Restricted Neutrosophic Set and Its Application

This paper is devoted to introduce a novel concept known as restricted neutrosophic set (RNS) as another subclass of neutrosophic set (NS). The purpose of introducing the notion of RNS is to give a new mathematical theory that is more promising and purposeful than the existing fuzzy-centric theories to solve the uncertainty based real-world problems in a lucid manner. From decision-makers point of view, the new mathematical tool can be viewed as a direct extension of Pythagorean neutrosophic set (PNS). The PNS has its own inherent limitation for which the decision-makers can’t answer a certain type of problem. For example, in a certain problem, if we consider the degree of truth-membership =0.8, degree of indeterminate-membership , and the degree of falsity-membership =0.8, then it gives an absurd result under PNS. To remove such kind of absurdity, there is a demand to introduce another superior set-theoretical concept that provides more information for the decision-makers. This gives rise to the introduction of RNS. In RNS, we choose any value belongs to for the three membership degrees so that their product always limited to 1. So, the beauty of RNS is that it can accommodate more information within small range with relaxed membership values i.e under RNS we can consider the maximum membership triplet as . Undoubtedly the RNS gives more compact set-theoretical model to describe imprecise knowledge with ease. Finally, a decision-making approach based algorithm is introduced and applied to solve medical diagnosis problem.

Attention-Based Neural Bag-of-Features Learning for Sequence Data

In this paper, we propose 2D-Attention (2DA), a generic attention formulation for sequence data, which acts as a complementary computation block that can detect and focus on relevant sources of information for the given learning objective. The proposed attention module is incorporated into the recently proposed Neural Bag of Feature (NBoF) model to enhance its learning capacity. Since 2DA acts as a plug-in layer, injecting it into different computation stages of the NBoF model results in different 2DA-NBoF architectures, each of which possesses a unique interpretation. We conducted extensive experiments in financial forecasting, audio analysis as well as medical diagnosis problems to benchmark the proposed formulations in comparison with existing methods, including the widely used Gated Recurrent Units. Our empirical analysis shows that the proposed attention formulations can not only improve performances of NBoF models but also make them resilient to noisy data.

Some Linear Diophantine Fuzzy Similarity Measures and Their Application in Decision Making Problem

The uncertainty in the data information for decision making is a most challenging and critical fear. In order to reduce the uncertainty in the decision making expert information for decision making problem, the Linear Diophantine fuzzy number is taking more critical part in reducing the uncertainty in information. Therefore the primary aim of this paper is to develop some different types of similarity and distance measures for linear Diophantine fuzzy numbers. With the frequent occurrence of emergency events, emergency decision making (EDM) plays a significant role in the emergency situations. It is essential for decision makers to make reliable and reasonable emergency decisions within a short time period since inappropriate decisions may result in enormous economic losses and chaotic social order. Accordingly, to ensure that EDM problems can be solved effectively and quickly, this paper proposes a new EDM method based on the novel distance and similarity measures under Linear Diophantine fuzzy (LDF) information. The similarity measure is one of the beneficial tools to determine the degree of similarity between objects. It has many crucial applications such as decision making, data mining, medical diagnosis, and pattern recognition. In this study, some novel distances and similarity measures of linear Diophantine fuzzy sets are presented. Then, the Jaccard similarity measure, exponential similarity measure, Cosine and Cotangent function based on similarity measures for LDFSs were proposed. The newly defined similarity measures are applied to medical diagnosis problem for COVID-19 virus and the results are discussed. A comparative study for the new similarity measures is established, and some advantages of the proposed work are discussed.

A New Approach towards Intuitionistic Fuzzy Multisets

In this paper a new definition of Intuitionistic fuzzy multisets (IFMS) has been introduced. Algebraic operations on these intuitionistic fuzzy multisets are defined and their properties under these algebraic operations are studied. The author has also introduced a new notion of complement for an IFMS in which the complement of the original set is also an IFMS. The notion of distance and similarity between two IFMS’s has been defined and their properties have also been studied here. An application of IFMS in solving a medical diagnosis problem has been provided at the end.

Generalized Pythagorean fuzzy sets and new decision-making method

The thought of the Pythagorean fuzzy soft set emerging from the Pythagorean fuzzy set, which is initiated by Yager, was generalized by including a parameter by Kirisci. In the present communication, a new decision-making method is defined. For this decision-making method, an algorithm is conceived. The reason for giving this algorithm is to facilitate the solution of multi-attribute decision-making difficulties. A numerical example concerning a medical diagnosis problem for the proposed algorithm has been successfully illustrated.

A combinatorial optimization approach for multi-label associative classification

Mining associations between variables corresponding to multiple class labels (or outcomes) is prevalent in various applied domains, such as medical diagnosis, text mining, e-commence, and social behavior analysis. While most associative classification algorithms have been developed to discover association rules of binary variables for single-label classification problem, there are limited methods designed for the problem, called multi-label classification, that accounts for multi-labels. In this study, we consider the multi-label classification problem as a multi-class classification problem and formulate it as a 0–1 integer optimization model. We then leverage combinatorial optimization and association rule techniques to solve this hard problem. More specifically, we propose a ranking metric for selecting and aggregating stronger rules to form an optimal multi-label classifier. The computational results for multiple real applications show that our algorithm is able to identify significant association rules between key variables and multiple labels, and in turn achieves a competitive classification performance compared to state-of-the-art machine learning methods such as logistic regression, decision trees, and random forest. Moreover, we design a user-interface tool interfaced with the developed algorithm and demonstrate a medical diagnosis problem to predict multiple high-risk subgroups during emergency care in practice.

Multi-criteria decision making and pattern recognition based on similarity measures for Fermatean fuzzy sets

Higher-order fuzzy decision-making methods have become powerful tools to support decision-makers in solving their problems effectively by reflecting uncertainty in calculations better than crisp sets in the last 3 decades. Fermatean fuzzy set proposed by Senapati and Yager, which can easily process uncertain information in decision making, pattern recognition, medical diagnosis et al., is extension of intuitionistic fuzzy set and Pythagorean fuzzy set by relaxing the restraint conditions of the support for degrees and support against degrees. In this paper, we focus on the similarity measures of Fermatean fuzzy sets. The definitions of the Fermatean fuzzy sets similarity measures and its weighted similarity measures on discrete and continuous universes are given in turn. Then, the basic properties of the presented similarity measures are discussed. Afterward, a decision-making process under the Fermatean fuzzy environment based on TOPSIS method is established, and a new method based on the proposed Fermatean fuzzy sets similarity measures is designed to solve the problems of medical diagnosis. Ultimately, an interpretative multi-criteria decision making example and two medical diagnosis examples are provided to demonstrate the viability and effectiveness of the proposed method. Through comparing the different methods in the multi-criteria decision making and the medical diagnosis application, it is found that the new method is as efficient as the other methods. These results illustrate that the proposed method is practical in dealing with the decision making problems and medical diagnosis problems.

New distance measure for Fermatean fuzzy sets and its application

As a new extended form of intuitionistic fuzzy sets, Fermatean fuzzy sets are powerful tools for describing vagueness and uncertainty in complex problems. In the method of handling Fermatean fuzzy information, the distance measure is an essential tool to depict the difference between two Fermatean fuzzy sets. However, how to accurately measure the distance between two Fermatean fuzzy sets is still a problem to be solved. In this paper, we devise two novel distance measure methods for Fermatean fuzzy sets. One is the distance measure of Fermatean fuzzy sets based on the Hellinger distance, which is called the FFSH distance. The other is the distance measure of Fermatean fuzzy sets based on the triangular divergence, which is called the FFSTD distance. Then, we prove that the proposed distance measure methods satisfy the axiomatic requirements of the distance function. Afterward, numerical examples are given to reveal that the proposed distance measures are more effective and reasonable than the normalized Euclidean distance measure, which can overcome the counter-intuitive situation. Besides, we utilize the proposed distance measure methods to address the problems of pattern recognition and medical diagnosis under Fermatean fuzzy environment and achieved excellent results. The experimental results illustrate that the proposed distance measure methods can efficiently handle the practical application under Fermatean fuzzy environment, and are more reliable than the normalized Euclidean distance measure.

Novel spherical fuzzy distance and similarity measures and their applications to medical diagnosis

Spherical fuzzy sets (SFSs) have recently become more popular in various fields. It was proposed as a generalization of picture fuzzy sets and Pythagorean fuzzy sets in order to deal with uncertainty and fuzziness information. The similarity measure is one of the beneficial tools to determine the degree of similarity between objects. It has many crucial application areas such as decision making, data mining, medical diagnosis, and pattern recognition. In the short time since their first appearance, some different distance and similarity measures of SFSs have been proposed, but they are limited through the literature. In this study, some novel distances and similarity measures of spherical fuzzy sets are presented. Then, we propose the Minkowski, Minkowski-Hausdorff, Weighted Minkowski and Weighted Minkowski-Hausdorff distances for SFSs. In addition, trigonometric and f-similarity measures are developed based on the proposed distances in this paper. The newly defined similarity measures are applied to medical diagnosis problem for COVID-19 virus and results are discussed. A comparative study of new similarity measures was established and some advantages of the proposed work are discussed.

A graph-based gene selection method for medical diagnosis problems using a many-objective PSO algorithm

BackgroundGene expression data play an important role in bioinformatics applications. Although there may be a large number of features in such data, they mainly tend to contain only a few samples. This can negatively impact the performance of data mining and machine learning algorithms. One of the most effective approaches to alleviate this problem is to use gene selection methods. The aim of gene selection is to reduce the dimensions (features) of gene expression data leading to eliminating irrelevant and redundant genes.MethodsThis paper presents a hybrid gene selection method based on graph theory and a many-objective particle swarm optimization (PSO) algorithm. To this end, a filter method is first utilized to reduce the initial space of the genes. Then, the gene space is represented as a graph to apply a graph clustering method to group the genes into several clusters. Moreover, the many-objective PSO algorithm is utilized to search an optimal subset of genes according to several criteria, which include classification error, node centrality, specificity, edge centrality, and the number of selected genes. A repair operator is proposed to cover the whole space of the genes and ensure that at least one gene is selected from each cluster. This leads to an increasement in the diversity of the selected genes.ResultsTo evaluate the performance of the proposed method, extensive experiments are conducted based on seven datasets and two evaluation measures. In addition, three classifiers—Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—are utilized to compare the effectiveness of the proposed gene selection method with other state-of-the-art methods. The results of these experiments demonstrate that our proposed method not only achieves more accurate classification, but also selects fewer genes than other methods.ConclusionThis study shows that the proposed multi-objective PSO algorithm simultaneously removes irrelevant and redundant features using several different criteria. Also, the use of the clustering algorithm and the repair operator has improved the performance of the proposed method by covering the whole space of the problem.

A New Approach Towards Intuitionistic Fuzzy Multisets

In this paper a new definition of Intuitionistic fuzzy multisets (IFMS) has been introduced. Algebraic operations on these intuitionistic fuzzy multisets are defined and their properties under these algebraic operations are studied. The author has also introduced a new notion of complement for an IFMS in which the complement of the original set is also an IFMS. The notion of distance and similarity between two IFMS’s has been defined and their properties have also been studied here. An application of IFMS in solving a medical diagnosis problem has been provided at the end.

Decision-Making with Fuzzy Soft Matrix Using a Revised Method: A Case of Medical Diagnosis of Diseases

In this paper, we study the matrix representation of fuzzy soft sets, complement of fuzzy soft sets, product of fuzzy soft matrices and the application of fuzzy soft matrices in medical diagnosis presented by Lavanya and Akila. Additionally, a new method (max-min average) based on fuzzy reference function is introduced instead of the max-product method by Lavanya and Akila to extend Sanchez’s technique for decision making problems in medical diagnosis. Using the same data by Lavanya and Akila, the result shows that the new method gives more information about the medical status of the patients being considered in relation to a set of diseases.