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

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

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...

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Main Authors: Benjamin Segun Aribisala, Deirdre Edward, Godwin Ogbole, Onoja M. Akpa, Segun Ayilara, Fred 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 Ibinaye, Osahon Osawata, Adeniyi Sunday, Ijezie Chukwuonye, Carolyn Jenkins, Hemant K. Tiwari, Okechukwu Ogah, Ruth Y. Laryea, Daniel T. Lackland, Oyedunni Arulogun, Omotolani Ajala, Rufus Akinyemi, Bruce Ovbiagele, Steffen Sammet, Mayowa Owolabi
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Neuroscience Informatics
Subjects:
Stroke
SIREN
Sub-Sahara Africa
Risk factors
Machine learning
Artificial neural network
Online Access:http://www.sciencedirect.com/science/article/pii/S2772528625000317
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Summary: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 stroke occurrence employing major risk factors in Sub-Saharan Africa better than regression models. Machine Learning, especially Artificial Neural Network, is recommended to enhance Afrocentric stroke prediction models for stroke risk factor quantification and control in Africa.
ISSN:2772-5286

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