Application of Data Mining to Predict Diabetes Disease

Florencia Yapnata; Abdullah

doi:10.63017/jdsi.v4i1.134

Authors

Florencia Yapnata student
Abdullah

DOI:

https://doi.org/10.63017/jdsi.v4i1.134

Keywords:

classification algorithm, data mining, diabetes, machine learning, prediction

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.

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