Afeez Adekunle Soladoye is an AI and computer engineering researcher specialising in machine learning, explainable AI, computational intelligence and healthcare AI. His research applies advanced computational methods to disease prediction and diagnosis, with work spanning stroke, Alzheimer's disease, Parkinson's disease, leukaemia and infectious diseases. His interests particularly emphasise interpretable and optimised AI systems capable of addressing real-world healthcare challenges, including those affecting African populations.

Afeez Adekunle Soladoye is a computer engineering researcher and academic specialising in artificial intelligence, machine learning, computational intelligence, and their applications in healthcare. His research focuses particularly on developing intelligent and explainable computational models for disease prediction, diagnosis, and clinical decision support. His academic background includes postgraduate research at Federal University Oye-Ekiti, with work involving evolutionary algorithms, feature selection, explainable AI (XAI), and machine-learning optimisation using electronic medical records.

His healthcare AI research spans several important clinical areas. He has published work on stroke prediction in Sub-Saharan Africa, including the use of gated recurrent units and feature-selection techniques, as well as a systematic review examining machine-learning algorithms, datasets, and geographical gaps in stroke prediction research.

His wider research portfolio includes deep transfer learning for leukaemia detection, explainable machine-learning approaches for early Alzheimer’s disease detection, and meta-heuristic optimisation of machine-learning models for Parkinson’s disease prediction. He has also contributed to research applying AI to Lassa fever diagnosis using Nigerian clinical records, demonstrating a strong interest in developing AI approaches relevant to African healthcare settings.

Alongside research, Afeez contributes to academic development through teaching, undergraduate supervision and peer review, including reviewing AI and technology research for several academic journals.