Artificial Intelligence and Machine Learning for Neurological Disorders in Africa: Conceptual Framework for Implementation in Nigeria
DOI:
https://doi.org/10.51412/psnnjp.2026.030Keywords:
AI/ML, Neurological disorders, Nigeria, Tele-neurology, policy imperatives for neurology data integrationAbstract
Background: Neurological disorders are a leading cause of disability and mortality worldwide, with sub-Saharan Africa experiencing a disproportionate burden due to the coexistence of communicable and non-communicable neurological diseases. In Africa, Nigeria bears the highest burden of stroke, and some neurological disorders, compounded by limited access to neurologists, neurodiagnostic facilities, and advanced imaging technologies which results in delayed diagnosis, poor clinical outcomes, and the risk of morbidity and mortality. Artificial intelligence (AI) and machine learning (ML) offer significant opportunities to improve neurological diagnosis, yet their implementation in Nigeria remains fragmented and lacks a context-specific framework. The objective of this study is to review current evidence on the burden of neurological disorders and AI/ML applications in neurological diagnosis in Nigeria and Africa, and to propose a practical implementation framework tailored to Nigeria's healthcare system.
Methods: A narrative literature review was conducted using PubMed, Scopus, Google Scholar, and African scientific databases to identify peer-reviewed studies published between 2016 and 2026. Eligible studies evaluated AI/MLapplications for the diagnosis or prediction of neurological disorders in African populations or addressed implementation challenges relevant to low- and middle-income countries. Evidence was synthesized to identify implementation priorities and how to develop a health system-based framework.
Results: Available evidence demonstrates that AI/MLmodels applied to magnetic resonance imaging, computed tomography, electroencephalography, and clinical datasets can improve the detection and risk prediction of stroke, epilepsy, dementia, and other neurological disorders, with reported diagnostic accuracies promising, but subject to dataset bias and local infrastructure limitations. Major barriers to the implementation of these models include fragmented health information systems, inadequate digital infrastructure, limited funding, algorithmic bias, and shortages of skilled personnel.
Conclusion: A nationally coordinated AI/MLimplementation framework provides a feasible pathway for strengthening neurological diagnosis in Nigeria. Investment in digital health infrastructure, locally representative datasets, workforce capacity, and regulatory governance will be critical for translating AI innovations into equitable, scalable, and sustainable neurological care across Nigeria and other resource-constrained African settings.
References
1.Feigin VL, Vos T, Nichols E, Owolabi MO, Carroll WM, Dichgans M, Deuschl G, Parmar P, Brainin M and Murray C (2020) The global burden of neurological disorders: translating evidence into policy. The Lancet Neurology, 19 (3):255-265. https://doi.org/10.1016/S14744422(19)30411-9
2.World Health Organization (2025) Global status reporton neurology. Available at: https://www.who.int/publications/i/item/978924 0116139
3.Ngarka L, Siewe Fodjo JN, Aly E, Masocha W and Njamnshi AK (2022) The interplay between neuroinfections, the immune system and neurological disorders: a focus on Africa. Frontiers i n Immunology , 12 : 803475 . https://doi.org/10.3389/fimmu.2021.803475
4.T ekle-Haimanot R, Ngoungou EB and Kibru D (2018) Epidemiology of neurological disorders in sub-Saharan Africa. In Neuroepidemiology in Tropical Health, (pp. 129-141) Academic Press. https://doi.org/10.1016/B978-0-12-804607-4.00010-1
5.Logroscino G, Kalaria R and Preux PM (2024) Changing epidemiology of neurological diseases in Africa. Neuroepidemiology, 59 (4):291-293. https://doi.org/10.1159/000539654
6.Sarfo FS, Ovbiagele B, Gebregziabher M, Wahab K, Akinyemi R, Akpalu A, Akpa O, Obiako R, Owolabi L, Jenkins C and Owolabi M (2018) Stroke among young West Africans: evidence from the SIREN (Stroke Investigative Research and Educational Network) large multisite case-control study. Stroke, 49 (5):1116-1122. https://doi.org/10.1161/STROKEAHA.118.0207 83
7.GBD 2016 Stroke Collaborators (2019) Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology, 18(5): 439 - 4 5 8 . https://doi.org/10.1016/S1474-4422(19)30034-1
8.GBD 2019 Diseases and Injuries Collaborators (2020) Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 . The Lancet , 396 (10258): 1204 - 1222.
https://doi.org/10.1016/S0140-6736(20)30925-9
9.Kuate Tegueu C, Dzudie Tamdja A, Kom F, Forgwa Barche B, Ebasone P, Magnerou M, Mbonda P, Doumbe J, Husøy A, Thomas H and Steiner TJ (2024) Headache in the adult population of Cameroon: prevalence estimates and demographic associations from a cross-sectional nationwide population-based study. The Journal of Headache and Pain, 25 (1):42. https://doi.org/10.1186/s10194-024-01748-9
10.GBD 2021 Nervous System Disorders Collaborators (2024) Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet Neurology, 23(4): 344 - 381. https://doi.org/10.1016/S1474-4422(24)00038-3
11.Davis LE (2018) Acute bacterial meningitis. Continuum: Lifelong Learning in Neurology, 24 (5) : 1264 - 1283 .
https://doi.org/10.1212/CON.000000000000066 0
12.Mazamay S, Guégan JF, Diallo N, Bompangue D, Bokabo E, Muyembe JJ, Taty N, Vita TP and Broutin H (2021) An overview of bacterial meningitis epidemics in Africa from 1928 to 2018 with a focus on epidemics “outside-the-belt”. BMC Infectious Diseases , 21 ( 1 ): 1027 . https://doi.org/10.1186/s12879-021-06724-1
13.Aderinto N, Alare K, AbdulBasit OM, Edun M and Ogunleke P ( 2023 ) Prevalence of neuroinfectious diseases and outcomes in Africa. International Journal of Surgery: Global Health,6 ( 2 ) :e 1 0 4 . https://doi.org/10.1097/GH9.0000000000000104
14.Singhal BS and Khadilkar SV (2014) Neurology in the developing world. In Handbook of Clinical Neurology, 121: 1773 - 1782. https://doi.org/10.1016/B978-0-7020-4088-7.00114-0
15.Robertson FC, Lepard JR, Mekary RA, Davis MC, Yunusa I, Gormley WB, Baticulon RE, Mahmud MR, Misra BK, Rattani A, Dewan MC and Park KB (2018) Epidemiology of central nervous system infectious diseases: a meta-analysis and systematic review with implications for neurosurgeons worldwide. Journal of Neurosurgery, 130 (4): 1107 - 1126. https://doi.org/10.3171/2017.10.JNS17359
16.Nweke MC and Kanayo EC (2019) The place of spiritual and traditional beliefs in stroke rehabilitation in sub-Saharan Africa: a scoping review. Journal of Complementary and Alternative Medical Research, 8 (2):1-16. https://doi.org/10.9734/JOCAMR/2019/v8i2301 19
17.Ned L, Kpobi L and Ohajunwa C (2022) Thinking about mental health and spirituality from the Indigenous knowledge systems frame of reference. Disability Studies Quarterly, 41 (4). https://doi.org/10.18061/dsq.v41i4.8450
18.GBD 2019 Stroke Collaborators (2021) Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Neurology, 20(10): 795 - 820. https://doi.org/10.1016/S1474-4422(21)00252-0
19.Komolafe MA, Owagbemi OF and Alimi TI ( 2018 ) The distribution and pattern of neurological disease in a neurology clinic in Ile-Ife, Nigeria. Nigerian Journal of Clinical Practice, 21(11): 1520 - 1 5 2 4 . https://doi.org/10.4103/njcp.njcp_230_16
20.Elsayed M, Hamid OAA, Tonga R, Elsadig SM, Ahmed MH, Ahmed M and Saeed MK (2021) The pattern of neurological disorders; revisited prevalence data of a dedicated neurology clinic in Sudan. Case Reports in Clinical Medicine, 10 (11): 339 - 358. https://doi.org/10.4236/crcm.2021.1011043
21.Kelechi WE, Soyemi T, Mayowa S, Ede NS, Ubalaeze SE, Iloanusi CA, Agbo CE, Suzan IO, Augustine IC, Clinton IO, Akpowowo CK, Oyebola GF, Nndoumele C and Olabisi PL (2025) Building health care capacity for neurodegenerative disease management in Nigeria: challenges and opportunities. Journal of Public Health Research, 14(2): 22799036251350957.
https://doi.org/10.1177/22799036251350957
22.Adeloye D, Ezejimofor M, Auta A, Mpazanje RG, Ezeigwe N, Ngige EN, Harhay MO, Alemu W and Adewole IF (2019) Estimating morbidity due to stroke in Nigeria: a systematic review and meta-analysis. Journal of the Neurological Sciences, 402: 136 - 144.
https://doi.org/10.1016/j.jns.2019.05.020
23. Ezeala-Adikaibe BA, Orjioke C, Ekenze O, Ijoma U, Onodugo O, Molokwu O, Chime P, Mbadiwe N, Aneke E, Onyekonwu C, Okudo G, Okwara C, Onyebueke G and Ulasi I (2016) Prevalence of active convulsive epilepsy in an urban slum in Enugu South East Nigeria. Seizure, 35:100-105. https://doi.org/10.1016/j.seizure.2015.12.010
24.Aribisala BS, Edward D, Ogbole G, Akpa OM, Ayilara S, Sarfo F, Olabanjo O, Fakunle A, Macaulay BO, Yaria J, Akinyemi J, Akpalu A, Wahab K, Obiako R, Komolafe M, Owolabi L, Osaigbovo G, Okekunle AP, Singh A, Ibinaye P, Osawata O, Sunday A, Chukwuonye I, Jenkins C, Tiwari HK, Ogah O, Laryea RY, Lackland DT, Arulogun O, Ajala O, Akinyemi R, Ovbiagele B, Sammet S and Owolabi M (2025) Predicting stroke with machine learning techniques in a sub-Saharan African population. Neuroscience I n f o r m a t i c s , 5 ( 3 ) : 1 0 0 2 1 6 . https://doi.org/10.1016/j.neuri.2025.100216
25.Owolabi MO, Leonardi M, Bassetti C, Jaarsma J, Hawrot T and Makanjuola AI (2022) The neurology revolution. The Lancet Neurology, 21 (11):960-961. https://doi.org/10.1016/S1474-4422(22)00394-5
26.World Health Organization (2025) Stroke fact sheet. Available at: https://www.who.int/news-room/fact-sheets/detail/stroke
27.Pires AP, Martin KC, Secchi TL, Silva GS, Rebello LC, Bezerra DDC, de Freitas GR, Oliveira-Filho J, Miranda-Alves M, Kuster GW, Bazan R, Lange MC, de Carvalho JJF, Mont'Alverne FJA, Fiorot JA Jr, Zetola VF, Magalhães PSC, Rieder CRM, Nasi LA, Nogueira RG, Moro CC, Barbosa LA, Borelli WV, Pontes-Neto OM and Martins SCO (2026) Assessing the quality of stroke services in Brazil using the World Stroke Organization roadmap. Cerebrovascular D i s e a s e s , 5 5 ( 1 ) : 7 2 - 8 2 . https://doi.org/10.1159/000546276
28.Singh K and Malhotra J (2021) Cloud based ensemble machine learning approach for smart detection of epileptic seizures using higher order spectral analysis. Physical and Engineering Sciences in Medicine , 44 ( 1 ) : 313 - 324 . https://doi.org/10.1007/s13246-021-00970-y
29.Akyol K (2020) Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection. Expert Systems with A p p l i c a t i o n s , 1 4 8 : 1 1 3 2 3 9 . https://doi.org/10.1016/j.eswa.2020.113239
30.Jones KEA, Howells R, Mallick AA, Paul SP and Dey I (2023) NICE guideline review: epilepsies in children, young people and adults NG217. Archives of Disease in Childhood - Education and P r a c t i c e E d i t i o n , 1 0 8 ( 6 ) : 4 1 6 - 4 2 1 . https://doi.org/10.1136/archdischild-2022-324427
31.Eigenbrodt AK, Ashina H, Khan S, Diener HC, Mitsikostas DD, Sinclair AJ, Pozo-Rosich P, Martelletti P, Ducros A, Lantéri-Minet M, Braschinsky M, del Rio MS, Daniel O, Özge A, Mammadbayli A, Arons M, Skorobogatykh K, Romanenko V, Terwindt GM, Paemeleire K, Sacco S, Reuter U, Lampl C, Schytz HW, Katsarava Z, Steiner TJ and Ashina M (2021) Diagnosis and management of migraine in ten steps. Nature Reviews Neurology, 17 (8):501-514. https://doi.org/10.1038/s41582-021-00509-5
32.World Health Organization (2025) Dementia fact sheet. Available at: https://www.who.int/news-room/fact-sheets/detail/dementia
33.Jack CR Jr, Andrews JS, Beach TG, Buracchio T, Dunn B, Graf A, Hansson O, Ho C, Jagust W, McDade E, Molinuevo JL, Okonkwo OC, Pani L, Rafii MS, Scheltens P, Siemers E, Snyder HM, Sperling R, Teunissen CE and Carrillo MC (2024) Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup. Alzheimer's & Dementia, 20 (8):5143-5169. https://doi.org/10.1002/alz.13859
34.Ramantani G, Schmitt B, Plecko B, Pressler RM, Wohlrab G, Klebermass-Schrehof K, Hagmann C, Pisani F and Boylan GB (2019) Neonatal seizures-are we there yet? Neuropediatrics, 50 (5):280-293. https://doi.org/10.1055/s-0039-1693149
35.Kissani N, Liqali L, Hakimi K, Mugumbate J, Daniel GM, Ibrahim EAA, Yewnetu E, Belo M, Wilmshurst J, Mbelesso P, Ragab AH, Millogo A, Massimo L and Naji Y (2022) Why does Africa have the lowest number of neurologists and how to cover the gap? Journal of the Neurological Sciences, 434: 120119. https://doi.org/10.1016/j.jns.2021.120119
36.Howlett JR, Harlé KM, Simmons AN and Taylor CT ( 2025 ) Bayesian deconvolution for computational cognitive modeling of fMRI data. Neuro Image, 312: 121213. https://doi.org/10.1016/j.neuroimage.2025.1212 13
37.Frimpong M, Mohamed S, Ibukun MO, Owusu YA and Wireko AA (2026) Paediatric neurological care in Sub-Saharan Africa: current status and future directions. Brain and Development, 48 (1): 104490. https://doi.org/10.1016/j.braindev.2025.104490
38.World Health Organization (2022) Global health observatory data repository: MRI units per million population. Available at: https://www.who.int/data/gho/data/indicators/ind icator-details/GHO/total-density-per-million-population-magnetic-resonance-imaging
39.Elliot VL, Morgan D, Kosteniuk J, Froehlich Chow A and Bayly M (2019) Health-related stigma of noncommunicable neurological disease in rural adult populations: a scoping review. Health & Social Care in the Community, 27 (4):e158-e188. https://doi.org/10.1111/hsc.12694
40.Bakare MO, Frazier TW, Karpur A, Abubakar A, Nyongesa MK, Mwangi PM, Dixon P, Khaliq I, Gase NK, Sandstrom J, Okidegbe N, Rosanoff M, Munir KM and Shih A (2022) Brief report: validity and reliability of the Nigerian Autism Screening Questionnaire. Autism, 26 (6):1581-1590. https://doi.org/10.1177/13623613221080250
41.Odunyemi A, Islam MT and Alam K (2024) The financial burden of noncommunicable diseases from out-of-pocket expenditure in sub-Saharan Africa: a scoping review. Health Promotion International, 39(5): daae114 . https://doi.org/10.1093/heapro/daae114
42.Ofori B, Twum S, Yeboah SN, Ansah F and Sarpong KAN (2024) Towards the development of cost-effective point-of-care diagnostic tools for poverty-related infectious diseases in sub-Saharan Africa. Peer J, 12: e17198. https://doi.org/10.7717/peerj.17198
43.Charwa y-Felli A (2023) African neurology: a targeted and unified approach to interregional collaboration. Journal of the Neurological Sciences, 455: 120877. https://doi.org/10.1016/j.jns.2023.120877
44.Thakur K and Zunt J ( 2015 ) Tropical neuroinfectious diseases. Continuum: Lifelong Learning in Neurology, 21 (6):1639-1661. https://doi.org/10.1212/CON.000000000000024 8
45.Alfin JD, Mahmud MR, Yusuf AS, Binitie OP, Joseph SD, Bot GM and Yiltok SJ (2021) Acute traumatic spinal cord injury; does a low Tesla magnetic resonance imaging features correlates with neurological status and predict early outcome? Nigerian Journal of Medicine, 30 (6): 637 - 644. https://doi.org/10.4103/njm.njm_41_21
46.Nwazor E, Chukwuocha I, Ajuonuma B, Obi P and Madueke O (2024) A 2-year review of stroke admissions and short term out-come predictors in a teaching hospital, Southeast, Nigeria. Nigerian Medical Journal, 65(2): 185 - 194. https://doi.org/10.60787/nmj-v65i2-442
47.Arravalli T, Chadaga K, Muralikrishna H, Sampathila N, Cenitta D, Chadaga R and Swathi KS (2025) Detection of breast cancer using machine learning and explainable artificial intelligence. Scientific Reports, 15:26931. https://doi.org/10.1038/s41598-025-12644-w
48.Nomhwange T, Enegela J, and Ishaku A (2026) The utilization of artificial intelligence (AI) and machine learning (ML) for health in Nigeria: a rapid review. Journal of Medical Artificial Intelligence, 9: 2026 . https://doi.org/10.21037/jmai-2025-1-227
49.Bataliack S, Mbondji E, Karamagi H and Janauschek L (2024) Health data digitalization in Africa: unlocking the potential. AHOP Policy Briefs. Brazzaville: WHO Regional Office for Africa. Available at : https://ahop.aho.afro.who.int/download/health-data-digitalization-in-africa-unlocking-the-potential
50.World Health Organization (2025) Global status report on neurology. Available at: https://www.who.int/publications/i/item/978924 0116139
51.World Health Organization Regional Office for Africa (2024) Framework for integrating country and regional health data in the African Region (AFR/RC74/7). Available at : https://www.afro.who.int/sites/default/files/2024
-07/AFR-RC74-7%20Framework%20for%20integrating%20cou ntry%20and%20regional%20health%20data%20 in%20the%20African%20Region.pdf
52. Gianattasio KZ, Bennett EE, Wei J, Mehrotra ML, Mosley T, Gottesman RF, Wong DF, Stuart EA, Griswold ME, Couper D, Glymour MM and Power MC (2021) Generalizability of findings from a clinical sample to a community-based sample: a comparison of ADNI and ARIC. Alzheimer's & Dementia, 17 (8):1265-1276. https://doi.org/10.1002/alz.12293
53.Akpalu A, Sarfo FS, Ovbiagele B, Akinyemi R, Gebregziabher M, Obiako R, Owolabi L, Sagoe K, Jenkins C, Arulogun O, Adamu S, Appiah LT, Adadey MA, Agyekum F, Quansah JA, Mensah YB, Adeoye AM, Singh A, Tosin AO, Ohifemen O, Sani AA, Tabi-Ajayi E, Phillip IO, Isah SY, Tabari NA, Mande A, Agunloye AM, Ogbole GI, Akinyemi JO, Akpa OM, Laryea R, Melikam SE, Adinku D, Uvere E, Burkett NS, Adekunle GF, Kehinde SI, Azuh PC, Dambatta AH, Ishaq NA, Arnett D, Tiwari HK, Lackland D and Owolabi M (2015) Phenotyping stroke in sub-Saharan Africa: Stroke Investigative Research and Education Network ( SIREN) phenomics protocol. N euroepidemiology, 45(2): 73 - 82 . https://doi.org/10.1159/000437372
54.Ademuwagun IA, Adam Y, Rotimi SO, Syrbe S, Radtke M, Hentschel J, Lemke JR and Adebiyi E (2025) Exome sequencing in Nigerian children with early-onset epilepsy syndromes. Epilepsia O p e n , 1 0 ( 1 ) : 2 2 2 - 2 3 2 .
https://doi.org/10.1002/epi4.13106
55.Abiodun OA, Ajiboye PO, Salihu MO, Sulyman D, Akinsulore A, Obayi O and Salihu HB (2025) Psychiatrists' and t rainees' knowledge, perception, and readiness for integration of artificial intelligence in mental health care in Nigeria. BMC Psychiatry , 25 ( 1 ) : 685 . https://doi.org/10.1186/s12888-025-07135-1
56.National Institutes of Health (2021) NIH awards nearly $75M to catalyze data science research in Africa. Available at: https://www.nih.gov/news-events/news-releases/nih-awards-nearly-75m-catalyze-data-science-research-africa
57.Open Data for Africa (2025) Open Data for Africa data portal. Available at : https://dataportal.opendataforafrica.org/ydixvvd
58.Motshegwa T (2025) The African Open Science Platform: implementing the African Open Science Vision. Open Conference Proceedings, 5. https://doi.org/10.52825/ocp.v5i.1427
59.World Health Organization Regional Office for Africa (2025) Disease Prevention and Control (DPC) data portal. Available at: https://dataportal.afro.who.int/
60.World Health Organization Regional Office for Africa (2025) Integrated African Health Observatory(iAHO). Available at : https://aho.afro.who.int/about-us/af
61.Observational Health Data Sciences and Informatics (2025) OHDSI Africa. Available at: https://www.ohdsi.org/
62.Africa Health Data Space (2025) Africa Health Data Space. Available at : https://aun.mu.edu.et/ahds/
63.Adoga MP, Fatumo SA and Agwale SM (2014) H3Africa: a tipping point for a revolution in bioinformatics, genomics and health research in Africa. Source Code for Biology and Medicine, 9:10. https://doi.org/10.1186/1751-0473-9-10
64.Wogu E, Filima P, Caron B, Deabler D, Herholz P, Leal C, Mehboob MF, Kim S, Gosain A, Flexwala A, Hayashi S, Akintoye S, Ogoh G, Godwin T, Eke D and Pestilli F (2025) A labeled clinical-MRI dataset of Nigerian brains. Scientific Data, 12
(1):518. https://doi.org/10.1038/s41597-025-04743-0
65.Jiao F, Wang M, Sun X, Ju Z, Lu J, Wang L, Jiang J and Zuo C (2023) Based on tau PET radiomics analysis for the classification of Alzheimer's disease and mild cognitive impairment. Brain Sciences, 13(2): 3 6 7 . https://doi.org/10.3390/brainsci13020367
66.Shaikh MA, Al-Rawashdeh HS and Sait ARW (2025) A review of artificial intelligence-based Down syndrome detection techniques. Life, 15
(3):390. https://doi.org/10.3390/life15030390
67.McLaren JR, Yuan D, Beniczky S, Westover MB and Nascimento FA (2025) The future of EEG education in the era of artificial intelligence. E p i l e p s i a , 6 6 ( 6 ) : 1 8 3 8 - 1 8 4 2 . https://doi.org/10.1111/epi.18326
68.Tomašev N, Glorot X, Rae JW, Zielinski M, Askham H, Saraiva A, Mottram A, Meyer C, Ravuri S, Protsyuk I, Connell A, Hughes CO, Karthikesalingam A, Cornebise J, Montgomery H, Rees G, Laing C, Baker CR, Peterson K, Reeves R, Hassabis D, King D, Suleyman M, Back T, Nielson C, Ledsam JR and Mohamed S (2019) A clinically applicable approach to continuous prediction of future acute kidney injury. Nature, 5 7 2(7767 ) : 1 1 6 - 1 1 9 . https://doi.org/10.1038/s41586-019-1390-1
69.Attia ZI, Noseworthy PA, Lopez-Jimenez F,Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S and Friedman PA (2019) An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet, 394 (10201):861-867. https://doi.org/10.1016/S0140-6736(19)31721-0
70.Soladoye AA, Aderinto N, Popoola MR, Adeyanju IA, Osonuga A and Olawade DB (2025) Machine learning techniques for stroke prediction: a systematic review of algorithms, datasets, and regional gaps. International Journal o f Medical I n f ormatics , 2 0 3 : 1 0 6 0 4 1 . https://doi.org/10.1016/j.ijmedinf.2025.106041
71.Federal Republic of Nigeria (2023) Nigeria Data Protection Act, 2023 (Act No. 37). Federal Republic of Nigeria Official Gazette No. 119, Vol. 110. Lagos: The Federal Government Printer. Available at: https://ncc.gov.ng/media/801/view
72.Federal Ministry of Health, Nigeria (2025) Statement by Nigeria: agenda item 13.2, mental health and social connection. Seventy-eighth World Health Assembly. Available a t : https://apps.who.int/gb/statements/WHA78/PDF/Nigeria-13.2.pdf
73.Adewuya AO, Abdulmalik J, Abimbola S, Oladipo O, Shettima F, Dahiru A and Fasawe A (2026) Fidelity and feasibility of a pilot implementation of adapted WHO digital interventions to scale up mental health in Nigerian primary care: a mixed- methods process evaluation. Cambridge Prisms: Global Mental H e a l t h , 1 3 : e 6 5 . https://doi.org/10.1017/gmh.2026.10131
World Health Organization (2016) Global diffusion of eHealth: making universal health coverage achievable. Report of the third global survey on eHealth. Geneva: World Health O r g a n i z a t i o n . A v a i l a b l e a t : https://iris.who.int/bitstream/handle/10665/2525 29/9789241511780-eng.pdf
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
