Predicting Predisposition to Tropical Diseases in Female Adults Using Risk Factors: An Explainable-Machine Learning Approach

Predicting Predisposition to Tropical Diseases in Female Adults Using Risk Factors: An Explainable-Machine Learning Approach

Malaria, typhoid fever, respiratory tract infections, and urinary tract infections significantly impact women, especially in remote, resource-constrained settings, due to limited access to quality healthcare and certain risk factors. Most studies have focused on vector control measures, such as inse...

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Bibliographic Details
Main Authors: Kingsley Friday Attai, Constance Amannah, Moses Ekpenyong, Said Baadel, Okure Obot, Daniel Asuquo, Ekerette Attai, Faith-Valentine Uzoka, Emem Dan, Christie Akwaowo, Faith-Michael Uzoka
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Information
Subjects:
febrile disease
explainability
interpretability
LIME
machine learning
malaria
Online Access:https://www.mdpi.com/2078-2489/16/7/520
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