Predicting Heart Attacks
In the field of Medical Science, there are a huge amount of data. Data mining techniques are being used to discover hidden pattern form these data. Advance data mining techniques have been developed nowadays. The efficiency of these techniques is compared with sensitivity, specificity, accuracy and error rate. Some well known Data mining classification techniques, Decision Tree, Artificial neural networks, and Support Vector Machine and Naïve Bayes Classifier. In this paper, we introduce a new method based on the fitness value of the attribute to predict the heart disease problem. We use 10 attributes for our proposed method and use simple calculation. In our everyday life, there are several example exit where we have to analyze the historical data, for example, a bank loans officer needs analysis of her data in order to learn which loan applicants are “safe” and which are “risky” for the bank[1,2]. Similarly, for a medical researcher, it is necessary to analyze breast cancer data in order to predict specific treatments for a patient. These are some examples where the data analysis task required before taking any decision. Classification is a data analysis process, where a classifier is constructed to predict class, for bank loan example prediction class is “yes” or “no” Similarly for a medical researcher prediction class is “treatment A,” “treatment B,” or “treatment C” for the medical data[3,4]. Classification process can be divided into two parts (1)Learning: Training data are analyzed by a classification algorithm. Here, the class label attribute is loan decision, and the learned model or classifier is represented in the form of classification rules. (2) Classification: Test data are used to estimate the accuracy of the classification rules. If the accuracy is considered acceptable, the rules can be applied to the classification of new data tuples
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