Abstract: Aiming at  the problem of low prediction accuracy of traditional machine learning algorithms on Cleveland and Hungary dataset, we proposed a heart disease prediction method based on Bayesian hyperparameter optimization gradient boosting trees. Firstly, the K-nearest neighbor algorithm was used to fill in the missing values in the dataset, Min-Max standardization and One-Hot encoding were used  to process the data, and  the gradient boosting tree algorithm was used to predict the heart disease. Secondly, Bayesian optimization and ten-fold cross validation were used to search for the best combination of hyperparameters of the algorithm. The experimental results show that  the prediction accuracy of the optimized gradient boosting tree algorithm can reach 90.2% on the Cleveland heart disease dataset, and the prediction accuracy can reach 81.4% on the Hungarian heart disease dataset, outperforming  traditional machine learning methods such as decision tree, support vector machine and the K-nearest neighbor, it  can assist doctors in the diagnosis of heart disease.

Key words: heart disease prediction, K-nearest neighbor algorithm, gradient boosting tree, Bayesian optimization