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Abstract

Cardiovascular disease remains the cause of deaths worldwide. The percentage of premature death from this disease ranges from in high income countries and 42 % in low income countries. This shows the importance of heart predicting disease at the early stage. In this paper, a new unsupervised classification system is adopted for heart attack prediction at the early stage using the patient’s medical record. The information in the patient records are preprocessed initially using data mining techniques and then the attributes are classified using the Fuzzy C means classifier. In the classification stage 13 attributes are given as input to the Fuzzy C Means (FCM) classifier to determine a risk of heart attack. It is an unsupervised clustering algorithm, which allows one piece of the data to belong to two or more clusters. The proposed system will provide an aid for the diagnosis the disease in a more efficient way. The efficiency of the classifier is tested using the records collected from 1002 patients, which gives a classification accuracy of 94%. The result shows that the proposed clustering algorithm can predict the likelihood of patients and getting a heart attack in a more efficient and cost and effective way than the other well-known algorithms.

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How to Cite
P.Dhivya, N.Naveenraj, K.Savitha, C. Vijayakumar, & C. Vijayalakshmi. (2021). Intelligent heart disease prediction system using data mining techniques. International Journal of Intellectual Advancements and Research in Engineering Computations, 7(1), 1176–1180. Retrieved from https://ijiarec.com/ijiarec/article/view/270