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Cardiovascular risk prediction method based on test analysis and data mining ensemble system

Cardiovascular disease (CVD) is a highly significant contributor to loss of quality and quantity of life all over the world. Early detection and prediction is very important for patients' treatment and doctors' diagnose which can help to reduce mortality. In this paper, we focus on practical problem of Chinese hospital dealing with cardiovascular patients' data to make an early detection and risk prediction. To better understand the prescription and advice in Chinese, basic natural language processing method was used to synonym recognition and attribute extraction in Ultrasonic echocardiography. After data preprocessing, over 50 data mining techniques was tested for real patents dataset. Totake full advantage of multi-methods and reduce bias, top 6 subclassifiers was selected to form an ensemble system, adjusted voting mechanism was used to make a final result, which consists of risk prediction and confidence. System has a high precision of 79.3% for 2628 cases of real patents in experiment. Therisk prediction confidence and algorithm accuracy shows great significance in practical use for doctors' diagnosing.