Establishment and Optimization of Heart Disease Prediction Models Based on Machine Learning

One of the main causes of death in the globe is heart disease, and early prediction and intervention are crucial to reducing morbidity and mortality. Traditional diagnostic methods are susceptible to subjective factors and often result in misdiagnosis or missed diagnosis. According to the World Health Organization, myocardial infarction and stroke account for the majority of the 17.9 million heart disease-related deaths that occur each year. With the development of data analysis and machine learning technology, in-depth mining and pattern recognition of medical data have become possible. Studies have shown that machine learning models that combine indicators can significantly improve the accuracy of heart disease prediction. At the same time, precision medicine has also promoted the development of personalized treatment, which helps to improve intervention efficiency and patient quality of life. However, real clinical data have problems such as sample imbalance and feature redundancy, which often affect model performance and generalization ability. In order to achieve this, this study builds and evaluates five popular machine learning models using a substantial dataset of 10, 000 records pertaining to heart disease. Through feature optimization and dimensionality reduction processing, with the goal of identifying the best model solution and offering data assistance and decision-making reference for clinical diagnosis and risk warning, the performance of several algorithms in heart disease prediction tasks is assessed.