Predict Onset Age of Hypertension Using CatBoost and Medical Big Data

The development of IoT technology promotes the application of medical big data. Among which, early warning of chronic disease is of great significance to disease management. Hypertension is a widespread chronic disease. Preventing hypertension can effectively promote health conditions and reduce early mortality rate. In this paper, we attempt to predict the onset age of hypertension using CatBoost algorithm for supporting health management decision making based on medical big data. Firstly, the features that are highly associated with onset age of hypertension are analyzed by maximum information coefficient, and then those chosen features are used as the inputs of CatBoost algorithm to construct the prediction model of onset age of hypertension. 2363 sample data collected from a hospital in Beijing were used to verify the effectiveness of this approach. For the testing set, the RMSE was 5.38 and the MAPE was 9.42%, which outperforms linear regression model, SVM model and artificial neural network model. The experimental results show that the prediction model can predict individual's onset age of hypertension from current health indicators and provides a novel idea for early warning of hypertension.