Machine Learning-Based Model for Predicting Coronary Heart Disease Using Preβ HDL and Cytokines as Plasma Biomarkers

Coronary heart disease (CHD) remains the leading cause of global mortality, per the Center for Disease Control. Thus, it is important to develop novel and improved methods for CHD prediction, detection, and early intervention. Our study aims to assess the predictive efficacy of plasma Preβ High-Density Lipoprotein (HDL) and cytokines as biomarkers of CHD, utilizing machine learning (ML) algorithms to enhance risk predictions. In a case-control study, we explored the potential of 35 plasma cytokines in conjunction with Preβ HDL levels to discriminate "at risk" CHD patients from non-affected, control subjects. The dataset contains data on 108 individuals and is divided into two cohorts: 41 individuals with CHD and 67 individuals in the Control group. Leveraging random forest, coupled with feature engineering and importance techniques, the dataset underwent synthetic augmentation, yielding a total of 20,000 samples. In comparison to the Control group, individuals in the CHD group exhibited significantly higher levels of Plasma Preβ HDL, with mean values of 13.5 mg/dL apoA1 and 10.2 mg/dL apoA1 respectively (p < 0.05). The second random forest classifier incorporating: Preβ HDL, FGF-Basic, MCP-1, Eotaxin, IL-10, IL-9, IL-1β achieved a F1 score, prediction accuracy, and AUROC score of 100%. The remarkable results derived from the random forest classifiers underscore the need for further exploration into the predictive potential of Preβ HDL and plasma cytokines in the development of CHD, using ML methodologies. Further investigation may lead to the identification of novel drug targets for more effective therapeutic interventions.