Comprehensive multi-omics integration for cardiovascular metabolism prediction using machine learning

Abstract

CVDs cause more deaths than any other single disease worldwide, with an immense requirement for developing novel strategies in early risk stratification and personalized interventions. Pro viding real-time insights into small-molecule metabolites, metabolomics offers great potential perspectives on cardiovascular metabolism. Integrated in a multi-omics framework along with genomics and proteomics, metabolomics helps identify novel biomarkers and boost the predictive power. Actually, it is through the use of AI, particularly ML and DL, that enables to transform the prediction and diagnosis of CVD risk based on the efficient analysis of layers in complex data. This scoping review clusters literature into ML, DL, and statistical approaches but focuses on hybrid models integrating all these methodologies. Hybrid models that used RFE, CNNs, and statistical validation achieved the highest predictive accuracy, over 93 percent with high sen sitivity and specificity. DL models showed good performance with a mean accuracy of 92.5 percent, performing very well with high-dimensional data and predicting uniformly across dif ferent datasets. ML models enabled the interpretation of features by feature importance analysis that went further in the discovery of biomarkers and clinical utility. This validated the biological relevance of findings obtained, thus providing strong and dependable insights. The risk stratification of CVD and precision medicine are highly advanced by integrating AI driven metabolomics into multi-omics frameworks. These hybrid approaches reach an unprece dented degree of accuracy, interpretability, and clinical relevance with ML, DL, and statistical methods and hold the transformative power for early detection and personalized treatment of cardiovascular care.