Advancing Predictive Insights in Cardiology: A Comparative Analysis of Machine Learning Models for Early Detection and Clinical Implications of Heart Disease.

This research uses data to find patterns that may point to a higher risk of cardiovascular diseases and to use machine learning (ML) models to forecast heart disease, a significant cause of death worldwide (World Health Organization, 2023). Based on patient health data, we evaluate the effectiveness of many machine learning (ML) models in predicting heart disease, such as Support Vector Machines, K-nearest neighbors, Random Forest, and Logistic Regression. According to Smith and Patel (2022) and Johnson, Gupta, and Kumar (2021), the research method includes data preprocessing, exploratory data analysis, and a thorough assessment of each model's predicted accuracy. By enabling early intervention and individualized treatment regimens, our research suggests that machine learning (ML) can significantly improve the early diagnosis of cardiac illness, potentially transforming patient care and healthcare methods (Lee Kim, 2020). This study emphasizes how machine learning (ML) can revolutionize healthcare, especially in heart disease prediction. This will help lower the worldwide heart disease burden and enhance customized therapy (Carter et al., 2023). Keywords: Cardiovascular, predicting, machine learning, Logistic Regression, K-nearest neighbors, Random Forest, and Support Vector Machines.