The Exploring feature selection techniques on Classification Algorithms for Predicting Type 2 Diabetes at Early Stage

The Exploring feature selection techniques on Classification Algorithms for Predicting Type 2 Diabetes at Early Stage

Predicting early Type 2 diabetes (T2D) is critical for improved care and better T2D outcomes. An accurate and efficient T2D prediction relies on unbiased relevant features. In this study, we searched for important features to predict T2D by integrating ML-based models for feature selection and class...

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Bibliographic Details
Main Authors: Mila Desi Anasanti, Khairunisa Hilyati, Annisa Novtariany
Format: Article
Language:English
Published: Ikatan Ahli Informatika Indonesia 2022-11-01
Series:Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Subjects:
type 2 diabetes
machine learning
feature selection
feature importance
Online Access:http://jurnal.iaii.or.id/index.php/RESTI/article/view/4419

Internet

http://jurnal.iaii.or.id/index.php/RESTI/article/view/4419

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