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. 2021 Dec 3;21(23):8095.
doi: 10.3390/s21238095.

A Fuzzy Rule-Based System for Classification of Diabetes

Khalid Mahmood Aamir  1 , Laiba Sarfraz  1 , Muhammad Ramzan  1   2 , Muhammad Bilal  3 , Jana Shafi  4 , Muhammad Attique  5
Affiliations

A Fuzzy Rule-Based System for Classification of Diabetes

Khalid Mahmood Aamir et al. Sensors (Basel). .

Abstract

Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.

Keywords: classification; diabetes; diabetes prediction; fuzzy logic; fuzzy rule-based system.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The framework of the proposed system.
Figure 2
Figure 2
Fuzzy membership values for variables.
Figure 3
Figure 3
Demonstrates the threshold values of both classifiers for the training phase. (a) shows the threshold values for classifier 1 and (b) shows the threshold values for classifier 2.
Figure 4
Figure 4
MFs. (a–c) shows MFs for class 1 while (d,e) shows MFs for class 2.
Figure 5
Figure 5
MFs for classifier 1.
Figure 6
Figure 6
MFs for classifier 2.
See this image and copyright information in PMC

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