A hybrid framework for heart disease prediction: review and analysis

According to WHO the mortality rates are higher in case of heart diseases in the world and it is increasing continuously. The main reasons suggested in the several research work are smoking, alcohol, obesity, diet and hereditary. In this paper we are analyzing several methods of heart disease classification and prediction so that we can detect it in the earlier stages. As it can be cured if it is detected in the early stage. For this we have examined a few systems displayed till now and in view of the study introduced before recommending some future bits of knowledge which may be a superior structure for finding. Factors, Prevention and early discovery are additionally talked about here. The main objective of this study is to discuss on hybrid techniques specially data mining and optimization. So that better decision making and prediction framework will be designed.