Assessing the Role of Machine Learning Algorithms in Enhancing Malaria Diagnosis Accuracy in Primary Healthcare Facilities in Sub-Saharan Africa

Nakalya Twamina T.

School of Applied Health Sciences Kampala International University Uganda

ABSTRACT

Malaria continues to be a major public health challenge in sub-Saharan Africa, where accurate and timely diagnosis is often hindered by limitations in traditional diagnostic methods. This review evaluated the role of machine learning (ML) algorithms in improving malaria diagnosis in primary healthcare settings. Specifically, it explores applications of ML in microscopic image analysis, rapid diagnostic test (RDT) optimization, and predictive modeling, with a focus on their potential to enhance diagnostic accuracy and decision-making in resource-limited environments. ML techniques, such as convolutional neural networks (CNNs) for image analysis and data-driven models for optimizing RDT interpretation, have shown promise in addressing inter-observer variability and improving test sensitivity and specificity. Furthermore, predictive modeling integrating clinical, demographic, and environmental data can help prioritize malaria cases and guide healthcare providers in making accurate diagnoses. Despite these advancements, challenges such as data limitations, infrastructure gaps, and ethical considerations remain significant barriers to widespread adoption. The methodology utilized in this review involved a comprehensive synthesis of current literature, examining empirical studies on ML applications in malaria diagnosis and assessing their feasibility in primary healthcare contexts. To overcome these challenges, the article suggested policy recommendations, including investments in data infrastructure, capacity building, and public-private partnerships. Ultimately, ML offers a promising solution to enhance malaria diagnostic capabilities, contributing to better health outcomes in endemic regions.

Keywords: Machine Learning, Malaria Diagnosis, Primary Healthcare, Rapid Diagnostic Tests (RDTs), Predictive Modeling.

CITE AS: Nakalya Twamina T. (2025). Assessing the Role of Machine Learning Algorithms in Enhancing Malaria Diagnosis Accuracy in Primary Healthcare Facilities in Sub-Saharan Africa. Research Output Journal of Engineering and Scientific Research 4(3): 50-54. https://doi.org/10.59298/ROJESR/2025/4.3.5054