A Hybrid Method to Predict Angina Pectoris through Mining Emergency Data

The Emergency Department (ED) has been frustrated by the problems of overcrowding, long waiting times and high costs over decades. With the development of computer techniques, various kinds of information systems have appeared and make people work more effectively, the Emergency Department Information System (EDIS) has been heralded as a "must" for the modern ED. This paper tries to build a hybrid method to predict angina pectoris in the form of EDIS. Based on the frameworks of patients flow in ED, real-world data were collected from the electronic medical records at the ED: more than 210000 records of 842 registered chest pain patients in total. By utilizing the data mining techniques, an expert system was proposed to help physicians with faster and more accurate decision making of diagnosis and lab test selections when they are diagnosing with angina pectoris patients.