Comparative Study of Fuzzy Rule-Based Classifiers for Medical Applications

Abstract

The use of machine learning in medical decision support systems can improve diagnostic accuracy and objectivity for clinical experts. In this study, we conducted a comparison of 16 different fuzzy rule-based algorithms applied to 12 medical datasets and real-world data. The results of this comparison showed that the best performing algorithms in terms of average results of Matthews correlation coefficient (MCC), area under the curve (AUC), and accuracy (ACC) was a classifier based on fuzzy logic and gene expression programming (GPR), repeated incremental pruning to produce error reduction (Ripper), and ordered incremental genetic algorithm (OIGA), respectively. We also analyzed the number and size of the rules generated by each algorithm and provided examples to objectively evaluate the utility of each algorithm in clinical decision support. The shortest and most interpretable rules were generated by 1R, GPR, and C45Rules-C. Our research suggests that GPR is capable of generating concise and interpretable rules while maintaining good classification performance, and it may be a valuable algorithm for generating rules from medical data.

Fuzzy Information Recognition and Translation Processing in English Interpretation based on Artificial Intelligence Recognition Technology

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Interval Type-3 Fuzzy Inference System Design for Medical Classification Using Genetic Algorithms

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Editorial: Biomedical Data in Human–Machine Interaction

“…Also, the use of higher-order moment functions and their spectral images in the frequency domain as informative characteristics in Brain-Computer Interfaces systems is justified. Anna Czmil in her paper "Comparative Study of Fuzzy Rule-based Classifiers for Medical Applications" [17] used machine-learning-based methods in medical decision support systems, which can significantly improve diagnostic accuracy and objectivity for clinical experts. In this paper, the author carried out a thorough comparison of 16 different fuzzy-rule-based algorithms, which were applied on data obtained from 12 medical datasets and real-world data.…”