Medical Diagnosis and Pattern Recognition Based on Generalized Dice Similarity Measures for Managing Intuitionistic Hesitant Fuzzy Information

Abstract

Pattern recognition is the computerized identification of shapes, designs, and reliabilities in information. It has applications in information compression, machine learning, statistical information analysis, signal processing, image analysis, information retrieval, bioinformatics, and computer graphics. Similarly, a medical diagnosis is a procedure to illustrate or identify diseases or disorders, which would account for a person’s symptoms and signs. Moreover, to illustrate the relationship between any two pieces of intuitionistic hesitant fuzzy (IHF) information, the theory of generalized dice similarity (GDS) measures played an important and valuable role in the field of genuine life dilemmas. The main influence of GDS measures is that we can easily obtain a lot of measures by using different values of parameters, which is the main part of every measure, called DGS measures. The major influence of this theory is to utilize the well-known and valuable theory of dice similarity measures (DSMs) (four different types of DSMs) under the assumption of the IHF set (IHFS), because the IHFS covers the membership grade (MG) and non-membership grade (NMG) in the form of a finite subset of [0, 1], with the rule that the sum of the supremum of the duplet is limited to [0, 1]. Furthermore, we pioneered the main theory of generalized DSMs (GDSMs) computed based on IHFS, called the IHF dice similarity measure, IHF weighted dice similarity measure, IHF GDS measure, and IHF weighted GDS measure, and computed their special cases with the help of parameters. Additionally, to evaluate the proficiency and capability of pioneered measures, we analyzed two different types of applications based on constructed measures, called medical diagnosis and pattern recognition problems, to determine the supremacy and consistency of the presented approaches. Finally, based on practical application, we enhanced the worth of the evaluated measures with the help of a comparative analysis of proposed and existing measures.

Intelligent Medical Diagnosis Reasoning Using Composite Fuzzy Relation, Aggregation Operators and Similarity Measure of q-Rung Orthopair Fuzzy Sets

“…Muthukumar and Krishnan [20] used weighted similarity measures in medical diagnosis, while Iancu [23] used similarity measures based on the Frank t-norms family. Mahmood, Jaleel, and Rehman [12] used trigonometric similarity measures in bipolar fuzzy sets, while Albaity and Mahmood [24] used generalized dice similarity measures in pattern recognition and medical diagnosis problems. Nonetheless, similarity measures need to be involved in more complex decision-making problems with risk, and many other fields under uncertainty [21].…”

“…. , L}, the similarity measures (22,23,24) for CS, S10, q-ROFC (S(p l ), d m ) between patient symptoms, and the set of symptoms that are characteristic for each diagnosis d m , where m ∈ {1, . .…”

“…Similarity measures are another way of decision-making in medical diagnosis. By applying Equation ( 22), ( 23) or (24), the final table of decision was calculated. According to Vlachos and Sergiadis (Equation ( 25)), the maximum value of the table indicated the dominant class (disease).…”

Enhancing Similarity and Distance Measurements in Fermatean Fuzzy Sets: Tanimoto-Inspired Measures and Decision-Making Applications

“…Hussian and Yang [51] put forward novel similarity measures for PFSs that are grounded in Hausdorff measures. Additionally, recent studies [52][53][54][55][56][57][58] have introduced numerous other similarity measures.…”

Hamacher interaction aggregation operators for complex intuitionistic fuzzy sets and their applications in green supply chain management

“…Furthermore, FS is a special case of IFS, if at (ψ ) 0. Further, Gohain et al [12] proposed the distance measures for interval-valued IFSs, Ejegwa and Ahemen [13] presented similarity measures for enhanced IFSs, Davoudabadi et al [14] derived the simulation approaches for IFSs, Ejegwa and Agbetayo [15] introduced the similarity-distance measures for IFSs, Salimian and Mousavi [16] presented the MADM technique based on IFSs, Mahmood et al [17] proposed the TOPSIS method and Hamacher Choquet integral operators for IFSs, Shi et al [18] developed the power operators for interval-valued IFSs, Garg et al [19] gave the Schweizer-Sklar prioritized operators for IFSs, Albaity et al [20] presented Aczel-Alsina operators for intuitionistic fuzzy soft set (IFSS). Ecer [21] derived the modified MAIRCA technique for IFSSs, Garg and Rani [22] presented the distance measures for IFSs, Khan et al [23] proposed the divergence measures for IFSs, and Gohain et al [24] discussed the distance measures for IFS and their applications in decisionmaking, pattern recognition, and clustering analysis.…”