Research on Cardiovascular Disease Risk Prediction Based on Machine Learning

Cardiovascular diseases are one of the leading causes of mortality worldwide, making timely prediction and diagnosis crucial for preventing disease progression and reducing the risk of death. Traditional diagnostic methods for cardiovascular diseases rely on blood pressure measurements, cholesterol level tests, and other cardiac function tests, which are often time-consuming and not sensitive enough to early disease changes. This study utilizes three machine learning algorithms: KNN, Decision Trees, and Random Forests, to model and predict risk factors for cardiovascular diseases. By analyzing a large dataset of patients with cardiovascular diseases, combined with cross-validation and confusion matrices to evaluate the performance of different models, the aim is to identify the optimal model for predicting cardiovascular diseases. The results indicate that the Random Forest model performs the best in predicting cardiovascular diseases, with an accuracy rate and F1 score of 0.85 and 0.82, respectively. Compared to traditional diagnostic methods for cardiovascular diseases, the prediction model based on machine learning significantly improves diagnostic accuracy, providing a strong scientific basis for the early diagnosis and prevention of cardiovascular diseases.