Application of Machine Learning Algorithms for Early Prediction of Cardiovascular Diseases through Laboratory Biomarkers - Essgir, Prem Kumar; Simha, Mulavagili Vijaya (Asian Journal of Biological and Life Sciences, 2026)
Cardiovascular Diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, accounting for a significant proportion of global deaths each year. Early detection and risk stratification are crucial for preventing disease progression and reducing mortality rates. Traditional diagnostic methods rely on clinical risk scores and laboratory investigations; however, these approaches may fail to capture complex nonlinear relationships between multiple biological variables. Machine Learning (ML), a subset of artificial intelligence, has emerged as a powerful tool capable of analyzing large datasets and identifying hidden patterns that contribute to disease prediction. Recent studies have demonstrated that ML algorithms integrated with laboratory biomarkers can significantly improve the early detection and prognosis of cardiovascular diseases. Biomarkers such as cardiac troponins, C-reactive Protein (CRP), lipoproteins, cytokines, and other molecular indicators provide valuable information about cardiovascular pathophysiology. When combined with ML algorithms including random forest, support vector machines, neural networks, and gradient boosting, these biomarkers enable predictive models with higher accuracy and sensitivity. This review explores the current progress in integrating machine learning with laboratory biomarkers for early cardiovascular disease detection. It discusses commonly used biomarkers, types of machine learning algorithms, model development strategies, clinical applications, challenges, and future prospects. The integration of ML-based predictive systems with routine laboratory testing has the potential to revolutionize personalized cardiovascular medicine and improve clinical decision-making.
Keywords: Artificial intelligence; Biomarkers; Cardiovascular diseases; Early detection; Machine; learning; Predictive modeling