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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Elsevier
2025-09-01
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| Series: | Neuroscience Informatics |
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| author | 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 |
| author_facet | 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 |
| author_sort | Benjamin Segun Aribisala |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d2a3c97136a1460f87f0ef7cba28d0a6 |
| institution | Kabale University |
| issn | 2772-5286 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
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| series | Neuroscience Informatics |
| spelling | doaj-art-d2a3c97136a1460f87f0ef7cba28d0a62025-08-22T04:58:47ZengElsevierNeuroscience Informatics2772-52862025-09-015310021610.1016/j.neuri.2025.100216Predicting stroke with machine learning techniques in a sub-Saharan African populationBenjamin Segun Aribisala0Deirdre Edward1Godwin Ogbole2Onoja M. Akpa3Segun Ayilara4Fred Sarfo5Olusola Olabanjo6Adekunle Fakunle7Babafemi Oluropo Macaulay8Joseph Yaria9Joshua Akinyemi10Albert Akpalu11Kolawole Wahab12Reginald Obiako13Morenikeji Komolafe14Lukman Owolabi15Godwin Osaigbovo16Akinkunmi Paul Okekunle17Arti Singh18Philip Ibinaye19Osahon Osawata20Adeniyi Sunday21Ijezie Chukwuonye22Carolyn Jenkins23Hemant K. Tiwari24Okechukwu Ogah25Ruth Y. Laryea26Daniel T. Lackland27Oyedunni Arulogun28Omotolani Ajala29Rufus Akinyemi30Bruce Ovbiagele31Steffen Sammet32Mayowa Owolabi33Department of Radiology, University of Chicago, Chicago, IL, USA; Department of Computer Science, Lagos State University, Lagos, Nigeria; Department of Computer Science, Oduduwa University, NigeriaDepartment of Radiology, University of Chicago, Chicago, IL, USADepartment of Radiology, University of Ibadan, Ibadan, NigeriaDepartment of Epidemiology and Medical Statistics, University of Ibadan, NigeriaDepartment of Radiology, University of Ibadan, Ibadan, NigeriaDepartment of Medicine, Kwame Nkrumah University of Science and Technology, Kumasi, GhanaDepartment of Computer Science, Lagos State University, Lagos, NigeriaDepartment of Public Health, College of Health Sciences, Osun State University, Osogbo, Nigeria; Department of Medicine, College of Medicine, University of Ibadan, Ibadan, NigeriaDepartment of Computer Science, Lagos State University, Lagos, NigeriaDepartment of Medicine, College of Medicine, University of Ibadan, Ibadan, NigeriaDepartment of Epidemiology and Medical Statistics, University of Ibadan, NigeriaDepartment of Medicine, University of Ghana Medical School, Accra, GhanaDepartment of Medicine, University of Ilorin Teaching Hospital, Ilorin, NigeriaDepartment of Medicine, Ahmadu Bello University, Zaria, NigeriaDepartment of Medicine, Obafemi Awolowo University Teaching Hospital, Ile-Ife, NigeriaDepartment of Medicine, Aminu Kano Teaching Hospital, Kano, NigeriaDepartment of Medicine, University of Jos, Jos, NigeriaDepartment of Food and Nutrition, Seoul National University, KoreaDepartment of Epidemiology and Biostatistics, Kwame Nkrumah University of Science and Technology, GhanaDepartment of Medicine, Ahmadu Bello University, Zaria, NigeriaDepartment of Epidemiology and Medical Statistics, University of Ibadan, NigeriaDepartment of Medicine, University of Ilorin Teaching Hospital, Ilorin, NigeriaDepartment of Medicine, Federal Medical Centre, Umuahia, NigeriaMedical University of South Carolina, Charleston, USAUniversity of Alabama at Birmingham, Birmingham, AL, USADepartment of Epidemiology and Medical Statistics, University of Ibadan, NigeriaDepartment of Medicine, Kwame Nkrumah University of Science and Technology, Kumasi, GhanaMedical University of South Carolina, Charleston, USADepartment of Health Promotion, University of Ibadan, Ibadan, NigeriaDepartment of Medicine, College of Medicine, University of Ibadan, Ibadan, NigeriaDepartment of Medicine, Federal Medical Centre, Abeokuta, Nigeria; Centre for Genomic and Precision Medicine, College of Medicine, University of Ibadan, NigeriaWeill Institute for Neurosciences, School of Medicine, University of California San-Francisco, USADepartment of Radiology, University of Chicago, Chicago, IL, USA; Corresponding author at: Department of Radiology, University of Chicago Medicine, 5841 S. Maryland Avenue, MC2026 Chicago, IL 60637, USA.Department of Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria; Centre for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Nigeria; Corresponding author at: Center for Genomics and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, University College Hospital, Ibadan, and Blossom Specialist Medical Center, Ibadan, Nigeria.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.http://www.sciencedirect.com/science/article/pii/S2772528625000317StrokeSIRENSub-Sahara AfricaRisk factorsMachine learningArtificial neural network |
| spellingShingle | 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 Predicting stroke with machine learning techniques in a sub-Saharan African population Neuroscience Informatics Stroke SIREN Sub-Sahara Africa Risk factors Machine learning Artificial neural network |
| title | Predicting stroke with machine learning techniques in a sub-Saharan African population |
| title_full | Predicting stroke with machine learning techniques in a sub-Saharan African population |
| title_fullStr | Predicting stroke with machine learning techniques in a sub-Saharan African population |
| title_full_unstemmed | Predicting stroke with machine learning techniques in a sub-Saharan African population |
| title_short | Predicting stroke with machine learning techniques in a sub-Saharan African population |
| title_sort | predicting stroke with machine learning techniques in a sub saharan african population |
| topic | Stroke SIREN Sub-Sahara Africa Risk factors Machine learning Artificial neural network |
| url | http://www.sciencedirect.com/science/article/pii/S2772528625000317 |
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