Machine Learning-Based Approach for HIV/AIDS Prediction: Feature Selection and Data Balancing Strategy

Machine Learning-Based Approach for HIV/AIDS Prediction: Feature Selection and Data Balancing Strategy

HIV/AIDS remains a significant global health challenge, requiring accurate predictive models for early detection and improved clinical decision-making. However, developing an effective predictive model faces challenges such as data imbalance and the presence of irrelevant features, which can comprom...

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Bibliographic Details
Main Authors: Abdul Mizwar A Rahim, Ahmad Ridwan, Bambang Pilu Hartato, Firman Asharudin
Format: Article
Language:English
Published: Politeknik Negeri Batam 2025-03-01
Series:Journal of Applied Informatics and Computing
Subjects:
machine learning classification
feature selection
random oversampling
hiv/aids prediction
ensemble learning.
Online Access:https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9125
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