Abstrakt

Surveillance of Heart Disease using Data Mining Technique

Jyotismita Talukdar

Diagnosis of diseases from the database available is one of the vital and intricate jobs in medicine. With the advent of time, people are becoming more and more vulnerable to several diseases due to several reasons. One of the most frequently found disease all across the globe is the heart disease. Almost 60% of world population become victim of this disease. In this paper, we are trying to find the most probable factors that may be responsible for a person suffering from heart disease. The whole process of data mining is being carried out on the data available for the patients suffering from heart disease. Rattle data mining tool is being used for performing the tasks of analyzing the data of the heart patients. The data is being partitioned into training and testing datasets. The next steps namely clustering and modeling is performed on the training datasets .The testing dataset is used to obtain the unbiased errors. We also find out the correlation of the attributes being used in the present study. After finding the relationship of several attributes of the datasets of the heart patients we give a detailed explanation through the use of rattle data mining tool. Finally, the optimal heart parameters related to heart problem are found out for quick and correct diagnosis.