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.

In this paper, cluster analysis and the XGBoost method are used to analyze the related symptoms of various types of young hypertensive patients, and finally guide patients to target treatment. Hypertension is a chronic disease that is common worldwide. The incidence of it is increasing, and the age level of patients is decreasing year by year. Effective treatment of youth hypertension has become a problem in the world. In this paper, young hypertension patients are classified into two groups by cluster analysis; the proportion of different hypertension related symptoms in each group of patients is then counted; and after verifying the prediction accuracy of the XGBoost model with 10-fold cross-validation, the accuracy of clustering is calculated by the XGBoost method. The final result shows that there are significant differences in symptomatic entropy between patients with type II hypertension and those with type I hypertension. Patients with type II hypertension are more likely to have symptoms of ventricular hypertrophy and microalbuminuria. Through this analysis, patients can have preventive treatment according to their own situation, and this can reduce the burden of medical expenses and prevent major diseases. Applying the data analysis into the medical field has great practical significance.

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With the development of big data technology, creating the ‘Digital Campus’ is a hot issue. For an increasing amount of data, traditional data mining algorithms are not suitable. The clustering algorithm is becoming more and more important in the field of data mining, but the traditional clustering algorithm does not take the clustering efficiency and clustering effect into consideration. In this paper, the algorithm based on K-Means and clustering by fast search and find of density peaks (K-CFSFDP) is proposed, which improves on the distance and density of data points. This method is used to cluster students from four universities. The experiment shows that K-CFSFDP algorithm has better clustering results and running efficiency than the traditional K-Means clustering algorithm, and it performs well in large scale campus data. Additionally, the results of the cluster analysis show that the students of different categories in four universities had different performances in living habits and learning performance, so the university can learn about the students’ behavior of different categories and provide corresponding personalized services, which have certain practical significance.