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Fuzzy Modeling for Medical Diagnosis: A Computational Intelligence-Based Approach
Subject area: Science,Engineering and Technology · Area of research: Technology
Abstract
Medical diagnosis is a complex process that frequently involves dealing with uncertain symptoms and subjective assessments, making accurate and timely decision-making a challenge for healthcare professionals. Traditional computational methods often struggle to interpret imprecise data, leading to diagnostic ambiguity. Fuzzy logic provides an effective solution by mimicking human reasoning and allowing for the representation of medical variables in degrees of truth rather than rigid classifications. This paper explores the development of an expert system utilizing fuzzy logic to support disease diagnosis, particularly in cases where symptoms and patient-reported data are inherently vague or variable. The study outlines the key components of a fuzzy inference system, including fuzzification, rule base development, inference mechanisms, and defuzzification. It also examines how expert knowledge can be systematically integrated to enhance diagnostic accuracy. By allowing medical data to be processed in a more flexible and adaptive manner, fuzzy expert systems can improve clinical decision-making, especially in scenarios where precise numerical values are unavailable. Furthermore, this paper discusses the advantages of fuzzy logic-based diagnosis, such as its ability to handle uncertainty, its adaptability to various medical conditions, and its potential for integration with artificial intelligence and machine learning models. However, challenges remain, including computational complexity, the need for expert-defined rules, and difficulties in integrating fuzzy systems with existing healthcare infrastructure. Finally, potential applications of fuzzy logic in different medical domains are explored, demonstrating its role in improving diagnostic processes and patient care in modern healthcare environment
Keywords
Fuzzy logic, medical diagnosis, Expert systems, Computational intelligence.
References
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How to cite this paper
@article{1707766,
author = {Gabriel Alexandre De Souza},
title = {Fuzzy Modeling for Medical Diagnosis: A Computational Intelligence-Based Approach},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {10},
pages = {222-225},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707766.pdf},
abstract = {Medical diagnosis is a complex process that frequently involves dealing with uncertain symptoms and subjective assessments, making accurate and timely decision-making a challenge for healthcare professionals. Traditional computational methods often struggle to interpret imprecise data, leading to diagnostic ambiguity. Fuzzy logic provides an effective solution by mimicking human reasoning and allowing for the representation of medical variables in degrees of truth rather than rigid classifications. This paper explores the development of an expert system utilizing fuzzy logic to support disease diagnosis, particularly in cases where symptoms and patient-reported data are inherently vague or variable. The study outlines the key components of a fuzzy inference system, including fuzzification, rule base development, inference mechanisms, and defuzzification. It also examines how expert knowledge can be systematically integrated to enhance diagnostic accuracy. By allowing medical data to be processed in a more flexible and adaptive manner, fuzzy expert systems can improve clinical decision-making, especially in scenarios where precise numerical values are unavailable. Furthermore, this paper discusses the advantages of fuzzy logic-based diagnosis, such as its ability to handle uncertainty, its adaptability to various medical conditions, and its potential for integration with artificial intelligence and machine learning models. However, challenges remain, including computational complexity, the need for expert-defined rules, and difficulties in integrating fuzzy systems with existing healthcare infrastructure. Finally, potential applications of fuzzy logic in different medical domains are explored, demonstrating its role in improving diagnostic processes and patient care in modern healthcare environment},
keywords = {Fuzzy logic, medical diagnosis, Expert systems, Computational intelligence.},
month = {April},
}