Analysis on the heart disease prediction model based on machine learning

Cardiovascular disease (CVD) has become the major cause of death worldwide. Today, countries are taking heart disease more and more seriously and are actively seeking solutions. However, it is extremely difficult to achieve a complete cure for heart disease with the current state of global healthcare. This paper analyzes the factors affecting heart disease and builds corresponding models through k-nearest neighbor classification, logistic regression models, decision trees, and random forest classification. Then the author compares their metrics to determine the performance of the models. The experimental results show that the characteristics under study all have some degree of influence on heart disease. Moreover, each analysis method and model have strengths and weaknesses, with random forests performing most satisfactorily. In fact, there is much potential for the improvement in cardiac prediction, including algorithm optimization and physician observation. By combining all methods, the efficiency and accuracy of forecasting can be further improved.