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Yapnata, Florencia
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Computer Science & IT Engineering

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Journal : Data Science Insights

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Application of Data Mining to Predict Diabetes Disease Yapnata, Florencia; Abdullah
Data Science Insights Vol. 4 No. 1 (2026): Journal of Data Science Insights
Publisher : Visi Media Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63017/jdsi.v4i1.134

Abstract

Early management of diabetes risk and paying attention to the factors that cause someone to have the potential for diabetes are important to minimize cases of diabetes sufferers. Monitoring of Pre-diabetes patients is characterized by increasing certain parameters in the medical record data feature which is an important dynamic part of this study. Data mining techniques in diabetes disease prediction are used to determine a patient's risk of diabetes more quickly and accurately. This study uses the Knowledge Discovery in Database model process consisting of several stages such as Data Selection, Preprocessing, Transformation, Data Mining and Evaluation. The techniques that can be used to overcome these problems are Data Mining using the Navies Bayes algorithm, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Random Forest have various evaluation results with several input datasets used.
Co-Authors Abdullah