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.
