Developing Risk Level Prediction Model and Clinical Decision Support System for Cardiovascular Diseases in Ethiopia

Abstract Cardiovascular diseases have become one of the severe health problems in both developing and developed countries. This research aimed to develop a risk level prediction model and clinical decision support system for CVD in Ethiopia using data mining techniques. A total of 4004 datasets were used to develop the model. Moreover, primary data was collected from the domain experts via interviews and questionnaires. The domain experts identified thirty-one risk factors, of which only eleven attributes were selected after experimentation to develop the model. Based on the result of experimentation, the model was developed by an unpruned J48 classifier algorithm which produced F-Measure 0.877, which is comparatively the best algorithm. The prototype system was developed by Visual C# studio tool. The developed prototype system helps health care providers to identify risk level CVD diseases. It was developed using a data mining technique, which can efficiently predict cardiovascular disease risk levels. However, developing the model by using more datasets and changing the default setting of WEKA, a data mining tool, will be the future work of this study.