Comparison of Performance Support Vector Machine Algorithm and Naïve Bayes for Diabetes Diagnosis

WATOMAKIN, DOMINIKUS BOLI and Emanuel, Andi Wahju Rahardjo (2019) Comparison of Performance Support Vector Machine Algorithm and Naïve Bayes for Diabetes Diagnosis. In: Proceedings 2019 5th International Conference on Science in Information Technology (ICSITech). UPN "veteran" Yogyakarta, Yogyakarta, Indonesia, pp. 89-94.

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Text (Dominikus Boli Watomakin, and Andi Wahju Rahardjo Emanuel) 14. Comparison of Performance Support Vector Machine Algorithm and Naïve Bayes for Diabetes Diagnosis.pdf Download (1MB) | Preview

Abstract

Handling in the health sector has now developed a lot in terms of information technology. Many studies in the field of information technology that help in accelerating the performance of management of a health agency or from a work of health workers who require fast and good decision making. In this study a comparison of algorithms was used to diagnose diabetes, which had been used from many previous studies. Support vector machines and naïve bayes become comparison algorithms carried out in this study. The purpose of this study was to look at the performance of the two algorithms and help health workers in better decision making. The level of accuracy, precision, sensivity and specificity of the two algorithms will be the main focus of this research. Comparisons were made using a diabetes dataset taken from the National0 Institute0 of0 Diabetes0 and0 Digestive0 and Kidney0 Diseases with a total sample data of 768 sample data. From the results of calculations and comparisons of support vector machine algorithms have a better average value compared to the naïve Bayes algorithm.

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