Feasibility Study and Practice of Machine Learning-Based Heart Disease Prediction

With heart disease becoming one of the more lethal diseases globally, early diagnosis and effective prediction are important in reducing mortality. In recent years, the application of machine learning techniques in the medical field has gradually increased, especially in the prediction of heart disease, which shows better prospects. This paper explores the feasibility of a machine learning-based predictive model for heart disease by analyzing clinical data from heart disease patients. In this paper, several machine learning algorithms (e.g., KNN, Logistic Regression, Random Forest, and XGBoost) are used to model heart disease prediction and the performance of different models is evaluated. Experimental results show that XGBoost outperforms other traditional algorithms in a number of metrics such as accuracy, precision, recall and F1-score. The research in this paper shows that the application of machine learning methods, especially integrated learning-based models, in heart disease prediction has high feasibility and practical value.