Background::Precise risk prediction of chronic diseases is essential for effective preventive care and management. Machine learning (ML) is a promising avenue to enhance chronic disease risk prediction; however, a comprehensive assessment of ML performance across various chronic diseases, populations, and health settings is needed.Methods::This meta-analysis aims to synthesize evidence on the performance of ML techniques for predicting the risks and outcomes of chronic diseases. A literature search was conducted through PubMed, Web of Science, Scopus, Science Direct, Medline, and Embase. Studies applying ML techniques to predict chronic disease risks or outcomes and reporting performance metrics were included. Two reviewers independently screened studies, extracted data, and assessed the risk of bias. Random-effects meta-analysis, subgroup analyses, and meta-regression were performed to estimate pooled performance and explore heterogeneity.Discussion::This meta-analysis provides a comprehensive evaluation of the performance of ML techniques in predicting the risks and consequences of chronic diseases. We reported the pooled estimates of performance metrics, such as the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and F1 score, for each chronic disease. Subgroup analyses and meta-regression identified factors that influence the performance of ML models, such as the ML algorithm, sample size, and data type. This meta-analysis synthesized evidence on ML techniques for chronic disease risk prediction, guiding the development of robust and generalizable ML-based tools. By identifying best practices and addressing challenges, this work advances predictive analytics in healthcare, facilitates translation into clinical practice, and ultimately improve patient outcomes.

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.

Background::Precise risk prediction of chronic diseases is essential for effective preventive care and management. Machine learning (ML) is a promising avenue to enhance chronic disease risk prediction; however, a comprehensive assessment of ML performance across various chronic diseases, populations, and health settings is needed.Methods::This meta-analysis aims to synthesize evidence on the performance of ML techniques for predicting the risks and outcomes of chronic diseases. A literature search was conducted through PubMed, Web of Science, Scopus, Science Direct, Medline, and Embase. Studies applying ML techniques to predict chronic disease risks or outcomes and reporting performance metrics were included. Two reviewers independently screened studies, extracted data, and assessed the risk of bias. Random-effects meta-analysis, subgroup analyses, and meta-regression were performed to estimate pooled performance and explore heterogeneity.Discussion::This meta-analysis provides a comprehensive evaluation of the performance of ML techniques in predicting the risks and consequences of chronic diseases. We reported the pooled estimates of performance metrics, such as the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and F1 score, for each chronic disease. Subgroup analyses and meta-regression identified factors that influence the performance of ML models, such as the ML algorithm, sample size, and data type. This meta-analysis synthesized evidence on ML techniques for chronic disease risk prediction, guiding the development of robust and generalizable ML-based tools. By identifying best practices and addressing challenges, this work advances predictive analytics in healthcare, facilitates translation into clinical practice, and ultimately improve patient outcomes.

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.