PERFORMANCE COMPARISON OF CLASSICAL ALGORITHMS AND DEEP NEURAL NETWORKS FOR TUBERCULOSIS PREDICTION

PERFORMANCE COMPARISON OF CLASSICAL ALGORITHMS AND DEEP NEURAL NETWORKS FOR TUBERCULOSIS PREDICTION

This study compares the performance of several classical machine learning algorithms and deep neural networks for the prediction of tuberculosis in the Democratic Republic of Congo (DRC), using a sample of 1000 cases including clinical and demographic data. The sample is divided into two sets: 80% f...

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Bibliographic Details
Main Authors: Gilgen Mate Landry, Rodolphe Nsimba Malumba, Fiston Chrisnovi Balanganayi Kabutakapua, Bopatriciat Boluma Mangata
Format: Article
Language:English
Published: Community Service Research Center (LPPM) STMIK Nusa Mandiri Jakarta 2025-01-01
Series:Techno Nusa Mandiri: Journal of Computing and Information Technology
Subjects:
convolutional neural networks
deep neural networks
machine learning algorithms
performance metrics
tuberculosis prediction
Online Access:https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/5609
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