Comparative analysis of machine learning models for malaria detection using validated synthetic data: a cost-sensitive approach with clinical domain knowledge integration

Comparative analysis of machine learning models for malaria detection using validated synthetic data: a cost-sensitive approach with clinical domain knowledge integration

Abstract Malaria remains a critical global health challenge requiring innovative diagnostic approaches. Machine learning offers promising solutions for automated detection, but systematic algorithm comparison using clinically validated data remains limited. We systematically compared five machine le...

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Bibliographic Details
Main Authors: Gudi V. Chandra Sekhar, Chekol Alemu
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Machine learning
Malaria detection
Synthetic data validation
Cost-sensitive classification
Clinical decision support
XGBoost
Online Access:https://doi.org/10.1038/s41598-025-10231-7
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https://doi.org/10.1038/s41598-025-10231-7

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