Predicting stroke with machine learning techniques in a sub-Saharan African population

作者：Benjamin S. Aribisala, Deirdre Edward, Godwin Ogbole, Onoja Akpa, Segun Ayilara, Fred Stephen Sarfo, Olusola Olabanjo, Adekunle Fakunle, Babafemi Oluropo Macaulay, Joseph Yaria, Joshua Akinyemi, Albert Akpalu, Kolawole Wahab, Reginald Obiako, Morenikeji Komolafe, Lukman Owolabi, Godwin Osaigbovo, Akinkunmi Paul Okekunle, Arti Singh, Philip Ibinaiye, Osahon Osawata, Adeniyi Sunday, Ijezie Chukwuonye, Carolyn Jenkins, Hemant K. Tiwari, Okechukwu S. Ogah, Ruth Laryea, Daniel T. Lackland, Oyedunni Arulogun, Omotolani Ajala, Rufus Akinyemi, Bruce Ovbiagele, Steffen Sammet, Mayowa Owolabi · 发表于：Neuroscience Informatics · 年份：2025 · DOI：10.1016/j.neuri.2025.100216 · 被引用次数：4 · 研究领域：Acute Ischemic Stroke Management、Artificial Intelligence in Healthcare、Stroke Rehabilitation and Recovery

Background Stroke is the second leading cause of death and the third leading cause of disability globally, including Africa, which bears its largest burden. Accurate models are needed in Africa to predict and prevent stroke occurrence. The aim of this study was to identify the best machine learning (ML) algorithm for stroke prediction. Methods We assessed medical data of 4,236 subjects comprising 2,118 stroke patients and 2,118 controls from the SIREN database. Sixteen established vascular risk factors were evaluated in this study. These are addition of salt to food at table during eating, cardiac disease, diabetes mellitus, dyslipidemia, education, family history of cardiovascular disease, hypertension, income, low green leafy vegetable consumption, obesity, physical inactivity, regular meat consumption, regular sugar consumption, smoking, stress and use of tobacco. From these, we also selected the 11 topmost risk factors using Population-Attributable Risk ranking. Eleven ML models were built and empirically investigated using the 16 and the 11 risk factors. Results Our results showed that the 16 features-based classification (maximum AUC of 82.32%) had a slightly better performance than the 11 feature-based (maximum AUC 81.17%) algorithm. The result also showed that Artificial Neural Network (ANN) had the best performance amongst eleven algorithms investigated with AUC of 82.32%, sensitivity of 71.23%, specificity of 80.00%. Conclusion Machine Learning algorithms predicted ...