4.6 Article

A Fuzzy Rule-Based System for Classification of Diabetes

期刊

SENSORS
卷 21, 期 23, 页码 -

出版社

MDPI
DOI: 10.3390/s21238095

关键词

diabetes; fuzzy logic; fuzzy rule-based system; diabetes prediction; classification

资金

  1. National Research Foundation of Korea grant funded by the Korean Government [2020R1G1A1013221]
  2. [11-71-5/NU-R/21]
  3. National Research Foundation of Korea [2020R1G1A1013221] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

A study developed an interpretable early diagnosis model for diabetes using fuzzy logic, combining with cosine amplitude method to construct two fuzzy classifiers, designing fuzzy rules and evaluating the model's performance on a public dataset with an accuracy of 96.47%. The proposed model showed high prediction accuracy, suggesting its potential applications in accurate diabetes diagnosis in the healthcare sector.
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.

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