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Erick Munala Sifuna

Publications (3)

Explainable AI for Malaria Diagnosis: Comparative Analysis of ML Models Using Random Forest Feature Selection and SHAP Interpretability

David Kipngetich Chepkonga, Amos Kipkorir Langat, Ebenezer Esenogho, Mohamed Abdirahman Jama, Collins Otieno Owuor, Winnie Jepkonga Kulei, Charles Otieno Ndede, Erick Munala Sifuna, Samuel Kipsang Kaptum, Fred Junior Onyango, John Kamwele Mutinda & Aymar AKILIMALI · Asian Journal of Research in Computer Science · 2026

Malaria remains one of the most significant causes of morbidity and mortality in tropical and subtropical regions, and timely diagnosis is essential for effective case management. Microscopy is the traditional parasitological reference standard, while rapid diagnostic tests (RDTs...

Open access Research Article 10.9734/ajrcos/2026/v19i8899

Implementation of Partial Cross Entropy Loss for Point-supervised Remote Sensing Image Segmentation

Loraine Mutune, Tecla Mutave Kyalo, John Kamwele Mutinda, David Kipngetich Chepkonga, Jackson Ndoto Munyao, Stacey Jabeha, Amos Kipkorir Langat, Charles Otieno Ndede, Collins Otieno Owuor, Erick Munala Sifuna & Mercy Cherop · Asian Journal of Research in Computer Science · 2026

Weakly supervised semantic segmentation has emerged as a promising approach for reducing the annotation burden associated with dense pixel-level labelling. Among weak supervision strategies, point supervision offers an attractive compromise by requiring only a small subset of lab...

Open access Research Article 10.9734/ajrcos/2026/v19i9902

Explainable AI for Breast Cancer Diagnosis: Comparative Analysis of ML Models Using Random Forest Feature Selection and SHAP Interpretability

John Kamwele Mutinda, Tecla Mutave Kyalo, Joyce Akhalakwa Mukolwe, Jackson Ndoto Munyao, Millicent Auma Omondi, Wycliffe Nzoli Nzomo, Titus Mutua Kioko, David Chepkonga, Samuel Kipsang Kaptum, Erick Munala Sifuna & Amos Kipkorir Langat · Asian Journal of Research in Computer Science · 2025

Breast cancer diagnosis is critical for improving patient outcomes, yet traditional methods face limitations such as invasiveness and human error. This study presents an explainable AI framework for breast cancer classification using six ML models: LR, NB, KNN, RF, SVC, and DT. S...

Open access Research Article 10.9734/ajrcos/2025/v18i10762