Establish A Predictive Model of Hypertension Complications

Health promotion and prevention of diseases have become the current trend. Health promotion includes health education and health policies, and the environment. It is about healthy behaviors [1]. Nowadays, society has changed from a pattern of mainly infectious diseases to a pattern of chronic diseases. The WHO has analyzed the death causes of people in the country recently. The top few diseases were caused by chronic diseases, cerebrovascular disease, diabetes, coronary heart disease, etc., are the common disease. These diseases have a wide spread, long duration, high cost and high health risk.The Health Promotion Administration in Taiwan, Ministry of Health and Welfare pointed out that hypertension is a major risk factor for heart disease, stroke, kidney disease, eye diseases and other diseases. According to statistics, the prevalence of hypertension in Taiwan over the age of 18 years is 24.1%, and about 4% people suffer from hypertension. The severity of hypertension affects the occurrence of complications. If the timing of complications can be known immediately or in advance, conducting early health education can enable patients to receive early treatment and can also reduce the medical resources used afterward.In this study, data mining technology was used to establish a predictive model for the complications of hypertension, and the data of subjective, objective, assessment and planning (SOAP) in the medical records was extracted, and the main influence factors were obtained through the decision tree and Bayesian classification method.