Study on heart disease prediction based on SVM-GBDT hybrid model

In the past ten years, heart disease has been the main cause of death among Chinese residents. At present, the more accurate way to diagnose heart disease is invasive examination - cardiac angiography. This diagnostic method may cause serious arrhythmia, and some people may be allergic to contrast agents. Therefore, certain manpower and material resources are required to monitor the patient's vital signs after angiography. So, if we can use other patient data information to predict whether a person has heart disease through machine learning, it will make a great contribution to the prevention and diagnosis of heart disease. For this reason, this paper proposes an SVM-GBDT hybrid model based on feature selection to predict the occurrence of heart disease. After data processing, the regression results are obtained from the SVM model, and then the important attributes are filtered through feature selection by setting variance thresholds. The regression results are combined with the results of feature selection, and the GBDT model is used for prediction analysis. The experimental results show that the svm-gbdt hybrid model presented in this paper performs better than the single model at multiple evaluation metrics. When compared with the prediction effect of other machine learning models, the hybrid model proposed in this paper also performs well. As a result, the SVM-GBDT hybrid model based on feature selection proposed in this paper can play a helpful role in the prediction and diagnosis of heart disease.