Diabetes Diagnosis And Level Of Care Fuzzy Rule-Based Model Utilizing Supervised Machine Learning For Classification And Prediction

Aug 4, 2026

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Reliable medical decision-making enables earlier detection of diseases, particularly diabetes, which can lead to severe long-term complications. With healthcare datasets supporting research and clinical workflows, gaps in dataset information can reduce decision accuracy and impact diagnostic reliability. Fuzzy logic addresses vagueness and uncertainty, while supervised machine learning and data mining improve classification and prediction. The study applies ten supervised algorithms to simulated fuzzy diabetes datasets and compares results using accuracy, precision, recall, F1-score, and confusion matrices.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

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Abstract

Introduction

Journal of Theoretical and Applied Information Technology ~~31~~s~~t March 2024. Vol.102. No 6~~ © Little Lion Scientific ISSN: 1992-8645 [www.jatit.org](www.jatit.org) E-ISSN: 1817-3195 DIABETES DIAGNOSIS AND LEVEL OF CARE FUZZY RULE-BASED MODEL UTILIZING SUPERVISED MACHINE LEARNING FOR CLASSIFICATION AND PREDICTION TEH NORANIS MOHD ARIS1, AZURALIZA ABU BAKAR2, NORMADIAH MAHIDDIN3, MASLINA ZOLKEPLI4 1, 4Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia 2Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi Selangor, Malaysia 3Computing Science Studies, College of Computing, Informatics and Media Studies, Universiti Teknologi Mara (UiTM) Pahang Branch, Raub Campus, 27600 Raub, Pahang, Malaysia [1](1nuranis@upm.edu.my)[nuranis@upm.edu.my](1nuranis@upm.edu.my) (corresponding author), [2](2azuraliza@ukm.edu.my)[azuraliza@ukm.edu.my](2azuraliza@ukm.edu.my), [3](3normadiah@uitm.edu.my)[normadiah@uitm.edu.my](3normadiah@uitm.edu.my), [4](4masz@upm.edu.my)[masz@upm.edu.my](4masz@upm.edu.my) ABSTRACT A reliable medical decision-making is essential to diagnose a disease. This assists medical practitioners to detect a disease at early stage especially diabetes that causes further health complications. The diversity and availability of healthcare datasets supports medical practitioners to use computer applications in the diagnosis process. There are many medical datasets available for research usage but these datasets lacks information that allows decisions to be made accurately, which have a major impact to diagnose a disease. Fuzzy logic has contributed to handle vagueness and uncertainty issues and one of the appropriate models for the development of medical diagnostics. Most computer applications use machine learning and data mining techniques to aid classification and prediction of a disease. Therefore, a fuzzy model based on machine learning and data mining is a vital solution. In this study, ten supervised machine learning algorithms namely the J48, Logistic, NaiveBayes Updateable, RandomTree, BayesNet, AdaBoostM1, Random Forest, Multilayer Perceptron, Bagging and Stacking are applied for a simulated diabetes fuzzy dataset, verified by medical experts. The fuzzy datasets provide adequate information on the type of diabetes diagnosis and level of care related to the type of diabetes diagnosis. All algorithms were compared based on the accuracy, precision, recall, F1-Score, and confusion matrix. Experiment results for diabetes diagnosis dataset indicate 100% accuracy for the eight algorithms except AdaBoostM1 which produced 79.82% accuracy and Stacking 67.89% accuracy. In addition, level of care dataset reveals the highest accuracy of 97.15% for MLP and Bagging algorithms and the lowest accuracy of 91.66% for stacking algorithm. Overall, the proposed fuzzy rule-based diabetes diagnosis and level of care fuzzy model works well with most of the machine learning algorithms tested. Therefore, the proposed fuzzy model is a useful aid in the decision-making process, specifically in the healthcare sector. Keywords: Decision-making, Fuzzy, Supervised Machine Learning, Classification, Prediction 1. INTRODUCTION A chronic condition is a long-lasting disease that can have a significant impact on a person’s quality of life, cost and even life expectancy. Diabetes is a chronic disease that occurs when the pancreas does not produce insufficient insulin with the increase of blood sugar. When the insulin is insufficient, the body cannot use the insulin effectively and the concentration of glucose in the blood increases. The high concentration of glucose in the blood is known as diabetes and can cause further complications such as heart disease, kidney failure, nerve damage and other problems related to feet, oral health, vis

FAQs

Why is early diabetes detection important in this study?

Early detection can save lives, and a trustworthy decision-making model is crucial for accurate diabetes diagnosis.

How does the proposed approach handle uncertainty in medical data?

Fuzzy logic is used to manage vagueness and uncertainty, improving the overall decision-making process.

Which supervised machine learning algorithms are evaluated for the fuzzy diabetes datasets?

Ten supervised algorithms are applied, including J48, Logistic, NaiveBayes Updateable, RandomTree, BayesNet, AdaBoostM1, Random Forest, Multilayer Perceptron, Bagging, and Stacking.

Aug 4, 2026

Upload by Aurora