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All Journal Semesta Teknika Journal of Electrical Technology UMY JOIV : International Journal on Informatics Visualization
Arrayyan, Ahmad Zaki
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Library & Information Science

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Journal : Semesta Teknika

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Naive Bayes for Diabetes Prediction: Developing a Classification Model for Risk Identification in Specific Populations Arrayyan, Ahmad Zaki; Setiawan, Hendra; Putra, Karisma Trinanda
Semesta Teknika Vol 27, No 1 (2024): MEI
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/st.v27i1.21008

Abstract

Depending on persuasive statistics, the increasing prevalence of diabetes worldwide is a huge challenge for individuals, families, and nations. According to International Diabetes Federation (IDF) projections, the number of adults with diabetes is expected to rise by an astounding 46% by 2045, to reach 783 million, or one in eight. In response to this growing concern, this research explores the implementation of the Naive Bayes algorithm for predicting diabetes, employing comprehensive data cleansing and randomization techniques. A systematic evaluation of the model's performance is conducted using several training and testing split ratios (65:35, 75:25, 85:15). The outcome showed that the model performed best at the 65:35 split ratio, with accuracy reaching its maximum of 88.16%, precision 0.883, recall 0.881, and f1-score 0.882.
Co-Authors Al-Kamel, Mujib Alrahman Chen, Hsing-Chung Hendra Setiawan Karisma Trinanda Putra, Karisma Trinanda Prasetyo Kusumo, Mahendro Pratama, Sakca Garda Prayitno Prayitno Putra, Karisma Sisdarmanto Adinandra, Sisdarmanto Wicaksana, Darma