A New Hybrid XGBSVM Model: Application for Hypertensive Heart Disease

Abstract

The changes in people's life rhythm and improvement in material levels that happened in recent years increased the number of people suffering from high blood pressure in the world. Therefore, as a cardiac complication of hypertension, the prevalence of hypertensive heart disease has increased annually, it has seriously endangered the safety of human life, and the effective prediction of hypertensive heart disease has become a worldwide problem. This paper uses the newly proposed XGBSVM hybrid model to predict whether hypertensive patients will develop hypertensive heart disease within three years. The final experiment proves that through this model, hypertensive patients can learn their risk of hypertensive heart disease within 3 years and then undergo targeted preventive treatment, thereby reducing the psychological, physiological and economic burden. This paper confirms that the machine learning can be successfully applied in the biomedical field, with strong real-world significance and research value.

Keywords

• XGBSVM
• hypertensive heart disease
• SVM
• XGBoost
• biomedicine