A Data Mining Approach for Predicting Causes of Accident Using Emergency Medical Data: The Case of St. Paul Hospital

Abstract

Emergency medical health problem is one of the critical challenges that affect many people in the world, especially in developing countries. As statistics shows, in African region in general and Ethiopia in particular, the emergency medical case situation is still worse and needs special attention particularly Road Traffic Accident (RTA). A lot of demographic and clinical (related) data is recorded about patients who come and receive treatment in the emergency medical service unit of the hospital. As these data are getting larger and larger, there can be a probability in which hidden, implicit and non-trivial knowledge exist within these data. So far it is recognized among scientific scholars that traditional statistical methods might not be good enough to discover such hidden knowledge from large and complex volume of data. This is where data mining becomes very important to mine such hidden, complex, necessary data to generate vital knowledge. The problem is to be able to handle this huge amount of data and information in such a way that they can identify what is important and be able to extract it from the accumulated data. Now a days, data mining technology is being used as a tool that provides the techniques to transform these mounds of data into useful information which in turn enables to derive knowledge for decision making. A number of data mining techniques and tools are available to perform this task. Thus, the purpose of this study is to explore the potential applicability of data mining techniques in predicting the cause of accidents based on emergency medical data by taking St. Paul Hospitals as a case. Some machine learning algorithms from WEKA software.