As death rate due to heart diseases is increasing significantly, prediction of heart disease with required accuracy becomes a critical issue in health care industry. Data mining and machine learning algorithms, more specifically classification algorithm plays an important role in prediction. Still the accuracy of prediction is influenced by the evolving size of data, nature or format of data and velocity of data. Keeping these factors in mind, Big data based model has been proposed after a careful investigation on existing analytical algorithms. In this paper, some of the existing literature related to the prediction of heart diseases using data mining is presented. Inferences are drawn to find out the essential attributes to be considered for prediction. A study is carried out to find which algorithm will work better for prediction of heart diseases. With inference drawn, an approach is proposed based on Hadoop and MapReduce programming paradigm. It is proposed to employ Support Vector Machine(SVM) in parallel fashion in order to improve the accuracy of prediction. The overview of proposed model is presented.

Bid data in prediction, classification techniques in heart disease prediction, parallel SVM

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