Medical data mining is a tremendously significant domain for exploration because of its importance in the expansion of innumerable applications in the medical domain. On the fact of briefing the deaths taking place globally, the heart disease seems as the foremost cause of death. The recognition of the chance of heart disease in an individual is a complex task for health specialists because it requires years of experience and intense medical tests to be conducted. In this research work, enhanced deep neural network (DNN) learning is introduced to treat patients accurately and for maintaining consistency in heart disease prediction system. So that anticipation of the loss of lives at the prior stage is possible. The results formulated ideally verify that the designed diagnostic scheme is able of calculating the risk level of heart disease efficiently when compared to other methodologies. The proposed model provides better results in heart diseases prediction compared to that of previous work. Early prediction of the disease reduces the costs and time of the treatment. The cost and time of treatment will be reduced due to the early prediction of heart disease.

Machine learning, Medical Data Mining, Heart Disease, Tensor Flow, Deep Neural Network

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