Transforming Preventive Healthcare with Machine Learning Technologies

Mumtaz Karatas; Zahra Zare; Yu-Jun  Zheng

doi:10.31181/jopi31202538

Authors

Mumtaz Karatas Biomedical, Industrial and Human Factors Engineering Department, Wright State University, Dayton, USA Author https://orcid.org/0000-0002-6287-3216
Zahra Zare Biomedical, Industrial and Human Factors Engineering Department, Wright State University, Dayton, USA Author https://orcid.org/0000-0002-8528-5716
Yu-Jun Zheng Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, China Author https://orcid.org/0000-0002-6095-6325

DOI:

https://doi.org/10.31181/jopi31202538

Keywords:

Machine learning, Preventive healthcare, Early disease detection, Risk prediction, Health analytics

Abstract

In this paper, we explore the transformative role of machine learning (ML) in preventive healthcare (PHC), a proactive approach to health management that aims to prevent diseases and promote overall well-being. We begin by providing an overview of PHC, its importance, and its applications across various healthcare settings. The manuscript then presents a brief review of ML techniques in this field, examining their potential to revolutionize early disease detection, personalized risk assessment, and targeted interventions. We review key studies that demonstrate the capabilities of ML in areas such as cancer screening, cardiovascular risk prediction, population health management, and traditional medicine. By synthesizing current research and identifying future directions, this work aims to enhance the understanding of how ML is reshaping the PHC domain, potentially improving health outcomes and reducing healthcare costs.

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