Heart Disease Prediction System Using CRISPADM and Decision Trees
Abstract
This paper aim is to average the use of techniques of decision trees, in combination with the management model CRISP-ADM, to help in the prediction of heart diseases. It is widely based on decision trees, an important concept in the field of artificial intelligence. This paper focuse on discussing how these trees are able to assist in the result making process of identifying heart diseases by the analysis of information provided from the hospitals. This information is captured with the help of techniques and the CRISP-DM management model of data mining in large prepared databases logged from hospital day to day transactions.