In the recent years, heart attack has become an alarming disease. Heart diseases have become one of the leading causes of death. In India, the number of deaths caused by heart attacks is about 25% of the total death. This happens due to the delay in detecting the symptoms or lack of early diagnosis. This can be avoided by integrating mobile computing technologies with health care systems, which will lead to detect abnormal heart rates and predict heart attacks before it occurs. Heart disease is a major cause of morbidity in the modern society. The earlier system detect the risk of heart attack using only limited parameter which are ECG, pulserate. Hence it cannot guarantee the risk for other symptoms like left shoulder pain, chest pain and etc. So the proposed system did consider all the parameter which can be a symptom of heart attack and hence provide a accurate risk detection system. The proposed system describes a heart attack self-test mobile application that allows potential victims, without the intervention of any medical specialist, to quickly assess whether they are having a heart attack.

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Nowadays the use of computer technology in the field of medical diagnosis and prediction of disease has increased. In these fields the computers are used with intelligence such as fuzzy logic, artificial neural network and genetic algorithms. Many techniques of data mining are useful in the field of medicine and many algorithms have been developed. The main objective of this work is to find out the important attributes which are highly important for accuracy of the classifier and reduce the dimensionality of dataset for classification of disease dataset. The other objective of this work is to classify the dataset in cost effective manner. As many tests are redundant and also are highly expensive. We have used various approaches for feature selection as using Brute force approach and correlation based approach. We have also proved that accuracy of classifiers are improved using feature selection.