Harnessing Machine Learning for Predictive Healthcare: A Path to Efficient Health Systems in Africa

Abstract

Abstract

Machine-learning (ML) presents a transformative opportunity to strengthen African health systems through predictive healthcare. This paper explores the applications, benefits, and implementation challenges of ML in African health contexts, where resource limitations and infrastructure gaps often impede efficient healthcare delivery. By leveraging supervised and unsupervised ML models—such as decision trees, neural networks, and support vector machines—predictive healthcare can aid in early disease detection, improve patient outcomes, and optimize resource allocation. Real-world case studies across the continent, including malaria forecasting and telemedicine applications, illustrate the potential of ML to mitigate the burdens of delayed diagnosis, an underutilized workforce, and a fragmented health infrastructure. However, barriers such as limited access to high-quality, structured health data, privacy concerns, algorithmic bias, and ethical dilemmas related to fairness and transparency must be addressed. The manuscript critically examines data preprocessing techniques, data source diversity, and the necessity of ethical frameworks for AI integration. Future directions include leveraging wearable technologies, integrating interdisciplinary research, and contextualizing ML models within Africa’s unique socio-political and epidemiological realities. The study argues for developing equitable, data-driven, and scalable ML solutions tailored to Africa’s public health priorities, shifting from reactive to predictive health systems.