Development of integrated machine learning models for multi-disease prediction
1 Department of Operations and Information Systems, Information Systems, University of Utah, Utah, USA.
2 Department of Medicine and Surgery, Edo State University, Uzairue, Edo, Nigeria.
3 Faculty of Health and Life Sciences, De Montfort University, Leicester, United Kingdom.
4 Department of Mathematics, Lagos State University, Lagos, Nigeria.
Review
World Journal of Biology Pharmacy and Health Sciences, 2023, 14(03), 384-390.
Article DOI: 10.30574/wjbphs.2023.14.3.0250
Publication history:
Received on 28 April 2023; revised on 06 June 2023; accepted on 08 June 2023
Abstract:
The increasing burden of infectious diseases on global public health systems necessitates innovative approaches for early prediction and intervention. This study focuses on the development of ensemble machine learning models to predict multiple infectious diseases, leveraging diverse datasets to provide actionable insights for public health policymakers. By integrating data from demographic, environmental, and clinical sources, the proposed models aim to identify high-risk individuals and regions, enabling targeted vaccination campaigns and optimized resource allocation during outbreaks. The results demonstrate the potential of machine learning in enhancing public health resilience, reducing disease transmission, and improving healthcare outcomes. This research contributes to the growing body of knowledge on data-driven decision-making in public health.
Keywords:
Machine Learning; Multi-Disease Prediction; Ensemble Models; Public Health; Infectious Diseases; Resource Allocation; Vaccination Campaigns
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0