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Artificial Intelligence in Health AI model for cardiovascular disease prediction

Academic Chief Technologist of the Laboratory for his 134: 672–681.
effort and validation of the results. https://doi.org/10.1016/j.amjmed.2020.10.017
Funding 5. Jibril IZ, Agajo J, Ajao LA, et al., 2018, Development of a
medical expert system for hypertensive patients diagnosis:
None. A knowledge-based rules. Adv Electron Eng J, 1: 23–29.
Conflict of interest 6. Wang S, Shen B, Martin C, 2021, Signal Analysis of Heart
Rate Variability and Applications on the Diagnosis of
The authors declare that they have no competing interest. Cardiovascular Diseases. In: MOL2NET, International
Conference Series on Multidisciplinary Sciences, p1–3.
Author contributions
7. Martin-Isla C, Campello VM, Izquierdo C, et al., 2020,
Conceptualization: Lukman Adewale Ajao, Buhari Ugbede Image-based cardiac diagnosis with machine learning:
Umar A review. Front Cardiovasc Med, 7: 1–3.
Formal analysis: Eustace Mananyi Dogo, Falilat Jumoke 8. Tarawneh M, Embarak O, 2019, Hybrid Approach for Heart
Ajao Disease Prediction Using Data Mining Techniques. In:
Investigation: Micheal Atama, Lukman Adewale Ajao International Conference on Emerging Internetworking,
Methodology: Micheal Atama, Lukman Adewale Ajao, Data and Web Technologies, p447–454.
Buhari Ugbede Umar 9. Khourdifi Y, Bahaj M, 2018, Applying Best Machine
Writing – original draft: Micheal Atama, Lukman Adewale Learning Algorithms for Breast Cancer Prediction and
Ajao, Buhari Ugbede Umar Classification. In: International Conference on Electronics,
Writing – review & editing: Eustace Mananyi Dogo, Falilat Control, Optimization and Computer Science (ICECOCS),
Jumoke Ajao, Lukman Adewale Ajao p1–5.
All authors equally contributed to the work. 10. Boateng D, Agyemang C, Beune E, et al., 2018, Cardiovascular

Ethics approval and consent to participate disease risk prediction in Sub-Saharan African populations
comparative analysis of risk algorithms in the RODAM
Not applicable. study. Int J Cardiol, 254: 310–315.

Consent for publication 11. Alaa AM, Bolton T, Di Angelantonio E, et al., 2019,
Cardiovascular disease risk prediction using automated
Not applicable. machine learning: A prospective study of 423,604 UK
Biobank participants. PloS One, 14: e0213653.
Availability of data https://doi.org/10.1371/journal.pone.0213653

The dataset used was collected from Heart Disease UCI 12. Mohan S, Thirumalai C, Srivastava G, 2019, Effective
Machine Learning Repository (http://archive.ics.uci.edu). heart disease prediction using hybrid machine learning
techniques. IEEE Access, 7: 81542–81554.
References
https://doi.org/10.1109/ACCESS.2019.2923707
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