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

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. 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. 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%. 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. • Stroke incidence is reducing in the Western world but increasing in Africa. • Accurate computational models are needed in Africa to predict and prevent stroke occurrence. • We proposed 11 machine learning models for predicting stroke in Africa. • Our results show that machine learning has a high predictive value. • Our results also identified 16 major risk factors for stroke in Africa.

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Publication Details

Journal
Neuroscience Informatics
Published
2025-06-17
DOI
https://doi.org/10.1016/j.neuri.2025.100216
Citations
3
Primary Topic
Acute Ischemic Stroke Management
Type
article
Field-Weighted Citation Impact
4.06
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Predicting stroke with machine learning techniques in a sub-Saharan African population

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Neuroscience Informatics
Acute Ischemic Stroke Management
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article

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

Olusola Aanu Olabanjo, Steffen Sammet, Philip Ibinaiye, Morenikeji Komolafe, Oyedunni Arulogun, Joshua Odunayo Akinyemi, Joseph Yaria, Rufus Akinyemi, Carolyn Jenkins, Lukman Femi Owolabi, Godwin Osawaru Osaigbovo, Mayowa Ojo Owolabi, Innocent Ijezie Chukwuonye, Okechukwu S. Ogah, Adekunle Fakunle, Kolawole Wahab, Onoja Matthew Akpa, Akinkunmi Paul Okekunle, Daniel T. Lackland, Babafemi Oluropo Macaulay, Arti Singh, Reginald Obiako, Ruth Laryea, Albert Kwaku Akpalu, Fred Stephen Sarfo, Benjamin S. Aribisala, Godwin Inalegwu Ogbole, Bruce Ovbiagele, Adeniyi Sunday, Omotolani Ajala, Deirdre Edward, Segun Ayilara, Osahon Osawata
article en
3 citations

Abstract

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. 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. 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%. 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. • Stroke incidence is reducing in the Western world but increasing in Africa. • Accurate computational models are needed in Africa to predict and prevent stroke occurrence. • We proposed 11 machine learning models for predicting stroke in Africa. • Our results show that machine learning has a high predictive value. • Our results also identified 16 major risk factors for stroke in Africa.

Neuroscience InformaticsVol. 5(3)
Lagos State University (NG), Ahmadu Bello University (NG), University of Ghana (GH), Seoul National University (KR), University of Jos (NG), University of Ibadan (NG), Kwame Nkrumah University of Science and Technology (GH), Oduduwa University (NG), Osun State University (NG), University of Chicago (US), Aminu Kano Teaching Hospital (NG), University of Ilorin Teaching Hospital (NG), Obafemi Awolowo University (NG)
Quality Education
Openalex Percentile: Top 6%
Acute Ischemic Stroke Management
4.06
DOIOpen Access PDFOpenAlex
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