Improving Diabetes Prediction Accuracy in Indonesia: A Comparative Analysis of SVM, Logistic Regression, and Naive Bayes with SMOTE and ADASYN

Improving Diabetes Prediction Accuracy in Indonesia: A Comparative Analysis of SVM, Logistic Regression, and Naive Bayes with SMOTE and ADASYN

This study aims to enhance the accuracy of diabetes prediction models in Indonesia by comparing the performance of Support Vector Machines (SVM), Logistic Regression, and Naïve Bayes algorithms, both with and without synthetic oversampling techniques such as SMOTE and ADASYN. The research addresses...

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Bibliographic Details
Main Authors: Selly Rahmawati, Arief Wibowo, Anis Fitri Nur Masruriyah
Format: Article
Language:English
Published: Ikatan Ahli Informatika Indonesia 2024-10-01
Series:Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Subjects:
adaptive synthetic sampling
diabetes mellitus
logistic regression
naïve bayes
synthetic minority over-sampling technique
support vector machine
Online Access:https://jurnal.iaii.or.id/index.php/RESTI/article/view/5980

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https://jurnal.iaii.or.id/index.php/RESTI/article/view/5980

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