Author Identifier
Ebenezer Afrifa-Yamoah: https://orcid.org/0000-0003-1741-9249
Eric Adua: https://orcid.org/0000-0002-6865-3812
Enoch O. Anto: https://orcid.org/0000-0001-9023-6612
Document Type
Journal Article
Publication Title
Chronic Diseases and Translational Medicine
Volume
11
Issue
1
First Page
1
Last Page
21
Publisher
Wiley
School
School of Medical and Health Sciences / School of Science
Publication Unique Identifier
10.1002/cdt3.137
RAS ID
70292
Abstract
Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these “omics” studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data-driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state-of-the-art applications of ML algorithms for chronic disease detection and prediction across datasets, including medical imaging, genomics, wearables, and electronic health records. Specifically, we review and synthesize key studies leveraging major ML approaches ranging from traditional techniques such as logistic regression and random forests to modern deep learning neural network architectures. We consolidate existing literature to date around ML for chronic disease prediction to synthesize major trends and trajectories that may inform both future research and clinical translation efforts in this growing field. While highlighting the critical innovations and successes emerging in this space, we identify the key challenges and limitations that remain to be addressed. Finally, we discuss pathways forward toward scalable, equitable, and clinically implementable ML solutions for transforming chronic disease screening and prevention.
DOI
10.1002/cdt3.137
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Comments
Afrifa‐Yamoah, E., Adua, E., Peprah‐Yamoah, E., Anto, E. O., Opoku‐Yamoah, V., Acheampong, E., ... & Hashmi, R. (2025). Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges. Chronic Diseases and Translational Medicine, 11(1), 1-21. https://doi.org/10.1002/cdt3.137