Every year 19 million people approximately die from heart disease worldwide. A heart patient shows several symptoms and it is very tough to attribute them to the heart disease in so many steps of disease progression. Data mining, as an answer to extract a hidden pattern from the clinical dataset, are applied to a database in this analysis. All available algorithms in classification technique are compared to each other to achieve the highest accuracy. To further increase the correctness of the solution, the dataset is preprocessed by different unsupervised and supervised algorithms. The two important tasks which are needed for the development of classifier come under data mining and they are clustering and classification. In K-means clustering the initial point selection effects on the results of the algorithm, both in the number of clusters found and their centroids. Methods to enhance the k-means clustering algorithm are discussed. With the help of these methods efficiency, accuracy and performance are improved. So, to improve the performance of clusters the Normalization which is a pre-processing stage is used to enhance the Euclidean distance by calculating more nearer centers, which result in a reduced number of iterations which will reduce the computational time as compared to k-means clustering. Finally, the classifiers are developed with Logistic regression by using the data extracted by K-Means Clustering. The techniques adopted in the design of classifier perform relatively well in terms of classification results better compared to clustering techniques.

Data mining, Classification techniques, K-means clustering, Neural Networks, Logistic Regression

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