Enhancing Cardiovascular Risk Prediction Using Support Vector Machines and Advanced Machine Learning Algorithms

A major cause of illness and mortality on a global scale is Cardiovascular Disease (CVD). Ensuring timely response and efficient management requires precise risk prediction. This study rigorously examines the use of support vector machines (SVM) and other advanced machine learning algorithms to opti-mize cardiovascular risk prediction. The authors have obtained a comprehensive dataset comprising patient gender analysis, patient behavior, and medical history parameters from a diverse patient cohort. The dataset has been meticulously preprocessed to impute missing values and accurately welcome and encode attributes. There were multiple machine learning models that were utilized, including SVM, random forest, and XG boosting, to forecast the likelihood of future cardiovascular events. This study’s key contributions include: (1) developing an optimized SVM model for cardiovascular risk prediction that outperforms traditional methods, (2) comprehensively comparing multiple advanced machine learning algorithms, and (3) introducing a novel approach to handling imbalanced data in cardiovascular risk assessment. The results unequivocally demonstrate that SVM, with its kernel technique and margin optimization, delivers superior performance in accuracy, precision, and recall compared to traditional logistic regression models. Ensemble methods such as Random Forest and XG Boosting are essential for managing imbalanced data and performing thorough feature importance analysis in data science and machine learning. This will enable doctors to easily understand each feature in predicting risk. In conclusion, the integration of machine learning algorithms, especially SVMs, into cardiovascular risk prediction frameworks can significantly enhance their predictive power. These advances have the potential to revolutionize CVD risk stratification and pave the way for personalized medicine, ultimately improving patient outcomes.