• V Geetha
• Dr S.Rajalakshmi

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 9 attributes. Three classifiers like Naive Bayes and Decision Tree algorithm are used to predict the diagnosis of patients with more accuracy as obtained before the reduction of number of attributes. . Classification tree uses many factors including age, blood sugar and blood pressure; it can detect the probability of patients fallen in CD by using fewer diagnostic tests which save time and money. It has been considered as an activist approach to improve the quality and accuracy of healthcare service while lowering the healthcare cost and diagnosis time. Using this technique presence of heart disease can be predicted accurately.

Data Mining, Decision tree, classification technique, Heart Disease, Prediction Model

"DECISION TREE ALGORITHM WITH RISK LEVELS TO ESTIMATE THE CRITICALITY OF CARDIOVASCULAR DISEASE ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.452-455, May-2019, Available :http://www.jetir.org/papers/JETIR1905I68.pdf

"DECISION TREE ALGORITHM WITH RISK LEVELS TO ESTIMATE THE CRITICALITY OF CARDIOVASCULAR DISEASE ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp452-455, May-2019, Available at : http://www.jetir.org/papers/JETIR1905I68.pdf