A Machine Learning Framework for Accurate Cardiovascular Risk Prediction Using Multivariate Clinical Features

Abstract

This study presents an intelligent predictive framework designed to assess the risk of heart-related conditions by analyzing multiple clinical and lifestyle attributes, including age, blood pressure, cholesterol concentration, smoking behavior, and other relevant medical indicators. The system utilizes supervised Machine Learning (ML) techniques to uncover hidden relationships within historical patient datasets and generate accurate risk estimations. In particular, Logistic Regression (LR) is employed as a probabilistic classification method that models the likelihood of disease presence based on input variables, offering interpretability and statistical robustness. Complementing this, Random Forest (RF), an ensemble-based algorithm, constructs multiple decision trees and aggregates their outputs to enhance predictive performance while minimizing overfitting. Additionally, Support Vector Machine (SVM) is incorporated to establish optimal decision boundaries by transforming input data into higher-dimensional feature spaces, thereby improving classification capability in complex scenarios. The integration of these algorithms forms a hybrid predictive model that balances interpretability, generalization, and accuracy. Through systematic training and evaluation, the framework demonstrates its effectiveness in identifying individuals at elevated risk of heart disease. Such a data-driven approach supports early detection, assists healthcare professionals in clinical decision-making, and promotes preventive healthcare strategies, ultimately contributing to improved patient outcomes and reduced burden on healthcare systems.