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...

Полное описание

Библиографические подробности
Главные авторы: Selly Rahmawati, Arief Wibowo, Anis Fitri Nur Masruriyah
Формат: Статья
Язык:English
Опубликовано: Ikatan Ahli Informatika Indonesia 2024-10-01
Серии:Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Предметы:
adaptive synthetic sampling
diabetes mellitus
logistic regression
naïve bayes
synthetic minority over-sampling technique
support vector machine
Online-ссылка:https://jurnal.iaii.or.id/index.php/RESTI/article/view/5980

Internet

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

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