Diagnosis of Coronary Heart Disease Through Fuzzy Information Measures and Pattern Recognition-Based Segmentation and Localization in Computed Tomography Angiography

Muhammad Rizwan Khan; Kifayat Ullah; Imran Siddique

doi:10.31181/sdmap56

Authors

Muhammad Rizwan Khan Department of Mathematics, Riphah International University Lahore, Lahore 54000, Pakistan Author https://orcid.org/0000-0002-1252-6654
Kifayat Ullah 1) Department of Mathematics, Riphah International University Lahore, Lahore 54000, Pakistan; 2) Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 602105, Tamil Nadu, India Author https://orcid.org/0000-0002-1438-6413
Imran Siddique Department of Mathematical, University of Sargodha, Sargodha 40100, Pakistan Author https://orcid.org/0000-0002-5060-7906

DOI:

https://doi.org/10.31181/sdmap56

Keywords:

Interval-valued spherical fuzzy set, Similarity measure, Multi-attribute decision-making sciences, Pattern recognition, Coronary heart disease

Abstract

Timely detection of coronary heart disease (CHD) is both significant and challenging. Advanced computational techniques such as similarity measures (SMs), entropy measures, and distance-measure-based segmentation can greatly enhance the precision, speed, and reliability of computed tomography angiography (CTA) image interpretation, thereby supporting more effective early detection and management of CHD. The concept of the interval-valued spherical fuzzy set (Iv-SFS) serves as a powerful tool for handling complex and ambiguous information, as it allows the representation of membership, abstinence, and non-membership values in the form of intervals. Building on the structure of Iv-SFS, this study introduces novel SMs, including interval-valued spherical fuzzy cosine SMs (Iv-SFCSM), Iv-SF cosine weighted SMs (Iv-SFCWSM), Iv-SF dice SMs (Iv-SFDSM), and Iv-SF dice weighted SMs (Iv-SFDWSM). Several axioms of SMs are demonstrated to verify the validity of the proposed measures. The developed SMs are then applied to solve a multi-attribute decision-making (MADM) problem for CHD diagnosis. Key features of the proposed approach are discussed, and comparative analyses with existing SMs highlight its advantages. The paper concludes with remarks underscoring the effectiveness and applicability of the proposed framework.

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