Now-a-days data mining is widely used in the medical field for analysis and diagnosis of disease. Various techniques such as clustering, classification, association of data mining are used to disclose unseen patterns from large number of datasets. Data mining techniques are applied on incompetent medical data recorded on daily basis. These techniques help to get useful information for diagnosing the diseases. Generally, numbers of tests are required to know the presence of a disease. In order to reduce these numbers of tests, data mining is utilized. In this paper, benign and malignant type of data for breast cancer disease has been used in which Benign tumour is non-cancerous tumour and malignant is cancerous tumour. In this research, two approaches are implemented in MATLAB for disease prediction. The first approach is based on k-means clustering and SVM algorithm for classification algorithm. In second approach, improved k-means clustering algorithm and SVM algorithm is implemented. The second approach gives better performance in terms of accuracy. Accuracy of classification of dataset depends upon the optimization of clustering and pre-processing of dataset.

Data Mining, K-means, SVM

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