Prediction of Heart Disease Based on Data Classification Models

With the increasing emphasis on healthy living, the prevention of major diseases has garnered widespread attention. Heart disease, as one of the leading causes of death globally, makes early prediction and identification extremely important. Currently, the prediction of heart disease mainly relies on a multidisciplinary approach. This article focuses on machine learning and selects four supervised learning classification models, including the Back Propagation (BP) neural network classification model, Genetic Algorithm (GA) -Back Propagation neural network classification model, Support Vector Machine (SVM) classification model and Random Forests Algorithm (RF) classification model, to evaluate their performance. Simulation experiments indicate that the GA-BP neural network classification model has the best generalization ability, while the RF classification model achieves the highest classification accuracy and recall rate, with an accuracy of 82.6% and a recall rate of 88.1% on the test set. Overall, the RF classification model performs the best among the four classification models. In future research, improvements and integrations of existing multiple data classification models will play a crucial role in enhancing classification accuracy.