Disease prediction using hybrid K-means and support vector machine

Medical data mining is one of the significant research field as medical organizations produce large volume of data on daily basis. Handling this vast amount of data in medical field is challenging, so there is a need to mine this data in order to extract useful patterns for disease prediction. A hybrid K-means and Support Vector Machine algorithm for disease prediction is proposed in this paper. The proposed hybrid K-means algorithm is helpful in choosing initial centroids, number of clusters and also to improve the efficiency of K-means algorithm. The hybrid K-means algorithm is used for dimensionality reduction of the dataset which is given as an input to Support Vector Machine classifier. The simulation is performed in MATLAB and from the results it has been analysed that the accuracy of the classification is improved and the processing time to obtain the final output is reduced.