Cardiac Catheterization Procedure Prediction Using Machine Learning and Data Mining Techniques
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2019, Vol 21, Issue 1
Abstract
Although catheterization is an important tool in the diagnosis and the treatment of cardiovascular diseases, it may cause different complications such as death or myocardial infarction during diagnosis. Data mining techniques are used for the construction of a cardiac catheterization Prediction System (CCPS) for whom catheterization is needed; therefore, it can decrease the complications of Cardiac catheterization Procedure. The aim of this study is to predict whether a patient needs a cardiac catheterization procedure or not. WEKA software was used in this experimental evaluation study of the Home dataset. Five classification algorithms were used for the prediction of catheterization procedure based on the prediction Accuracy, True Positive, True Negative, and ROC area. This study concluded that J48 without smoker attribute was a wellsuited model for the prediction of whether a patient needs a cardiac catheterization procedure or not.
Authors and Affiliations
Huda Kutrani, Saria Eltalhi
Keywords
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- EP ID EP444244
- DOI 10.9790/0661-2101018692.
- Views 114
- Downloads 0
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