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All Journal Indonesian Journal of Applied Technology and Innovation Science
Noviarni Noviarni
Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Comparison of Machine Learning Algorithms in Diabetes Risk Classification Zairy Cindy Dwinnie; Zaira Cindya Dwynne; Mohammed Jahidul Islam; Noviarni Noviarni
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 2 (2024): IJATIS August 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i2.1141

Abstract

Diabetes is a disease in which blood sugar levels are excessive without insulin control so that body functions do not function normally. Diabetes is also a disease that many people suffer from and is one of the main causes of death throughout the world. For this reason, we need to know the factors that are indicators of someone suffering from diabetes. This research compares the Decision Tree, Logistic Regression, and K-Nearest Neighbors algorithms with accuracy and Confusion Matrix parameters to determine diabetes sufferers in 520 data with the main indicator attributes supporting diabetes. From the test results of the three algorithms, the Decision Tree and K-Nearest Neighbors models have the highest accuracy of 86%. The Logistic Regression Algorithm has a fairly good accuracy of 83%.
Co-Authors Mohammed Jahidul Islam Zaira Cindya Dwynne Zairy Cindy Dwinnie