Data Mining Approach to Detect Heart Diseases
International Journal of Advanced Computer Science and Information Technology (IJACSIT) Vol. 2, No. 4, 2013, Page: 56-66
11 Pages Posted: 10 Jan 2014
Date Written: January 9, 2014
Globally, heart diseases are the number one cause of death. About 80% of deaths occurred in low- and middle income countries. If current trends are allowed to continue, by 2030 an estimated 23.6 million people will die from cardiovascular disease (mainly from heart attacks and strokes).
The healthcare industry gathers enormous amounts of heart disease data which, unfortunately, are not “mined” to discover hidden information for effective decision making. The reduction of blood and oxygen supply to the heart leads to heart disease. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. This research paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques which will be useful for medical practitioners to take effective decision. The objective of this research work is to predict more accurately the presence of heart disease with reduced number of attributes. Originally, thirteen attributes were involved in predicting the heart disease. Thirteen attributes are reduced to 11 attributes. Three classifiers like Naive Bayes, J48 Decision Tree and Bagging algorithm are used to predict the diagnosis of patients with the same accuracy as obtained before the reduction of number of attributes. In our studies 10-fold cross validation method was used to measure the unbiased estimate of these prediction models.
Keywords: Bagging algorithm, Data Mining, Heart disease Diagnosis, J48 Decision Tree, Naïve Bayes
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