Diabetes Diagnosis And Level Of Care Fuzzy Rule-Based Model Utilizing Supervised Machine Learning For Classification And Prediction
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 com...
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Main Authors: | , , , |
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Format: | Article |
Language: | English |
Published: |
Little Lion Scientific R&D
2024
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Online Access: | http://psasir.upm.edu.my/id/eprint/111491/1/JATIT%202024_27Vol102No6.pdf http://psasir.upm.edu.my/id/eprint/111491/ http://www.jatit.org/volumes/Vol102No6/27Vol102No6.pdf |
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Institution: | Universiti Putra Malaysia |
Language: | English |
Summary: | 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. |
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