A hybrid machine learning approach for hypertension risk prediction.

Hypertension is a primary or contributing cause for premature death in the entire world. As a matter of fact, there is a high prevalence and low control rates in low- and middle-income countries, such that the prevention and treatment of hypertension should remain a top priority in global health. In the recent years, the awareness, treatment, and control rates of hypertension patients in China have been significantly improved to 51.6%, 45.8%, and 16.8%, respectively. However, those rates are still far from a satisfactory level. Clinical studies suggest that for people in the pre-clinical stage of hypertension or having the risk of hypertension, the progression of the disease may be significanly reduced through a change in lifestyle, or by an effective drug therapy. In this paper, we address risk prediction for hypertension in the next five years, and put forward a model merging KNN and LightGBM. Our approach allows us to predict the hypertension risk for a specific individual using features such as the age of the subject and blood indicators. Results shows that our model is reliable and achieves accuracy and recall rate over 86% and 92%, respectively. [ABSTRACT FROM AUTHOR]