Abstract
Cardiovascular disease is a high-risk disease and therefore machine learning is needed to classify and predict it in order to aid research in the medical field. A prediction model for classifying cardiovascular patients based on an optimised random forest algorithm and comparing the prediction performance of each model. Using publicly available data on cardiovascular disease from the Kaggle platform, classification prediction models for cardiovascular disease were developed based on an integrated learning approach using Random Forest, Parsimonious Bayes, SVM and AdaBoost algorithms based on 12 indicators that may have an impact on the mortality of patients with cardiovascular disease. and classification prediction effects. Using the multiple averaging method to ensure the accuracy of the algorithms, the four types of AUROC values were observed and visualisation using matlab's powerful toolbox yielded the best ROC curve fit for random forest with an AUC value of 0.90.
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