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The diagnosis of heart disease has become a difficult medical task in the present medical research. This diagnosis depends on the detailed and precise analysis of the patient’s clinical test data on an individual’s health history. The enormous developments in the field of deep learning seek to create intelligent automated systems that help doctors both to predict and to determine the disease. Therefore, the Enhanced Deep learning assisted Convolutional Neural Network (EDCNN) has been proposed to assist and improve patient prognostics of heart disease. The EDCNN model is focused on a deeper architecture which covers multi-layer perceptron’s model with regularization learning approaches. Therefore, prompted for alternative methods such as machine learning algorithms that could use non-invasive clinical data for the heart Disease diagnosis and assessing its severity. Furthermore, the system performance is validated with full features and minimized features. Hence, the reduction in the features affects the efficiency of classifiers in terms of processing time, and accuracy has been mathematically analyzed with test results. The EDCNN system has been implemented Platform for decision support systems which help doctors to effectively diagnose heart patient’s information in cloud platforms anywhere in the world. The test results show compared to conventional approaches such as Multi-Layer Perceptron’s (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated recurrent units (GRU), Bidirectional Long Short-Term Memory (BiLSTM), Bidirectional Gated recurrent units (BiGRU) based on the analysis the designed diagnostic system can efficiently determine the risk level of heart disease effectively. Test results show that a flexible design and subsequent tuning of EDCNN hyper parameters can achieve a precision.


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Chimata Pravallika, Dasari Sarvani, Galla Sravanthi, & D.Kalaiabirami M.E. (2021). Heart Disease Prediction Using Convolutional Neural Network In Deep Learning. International Journal of Intellectual Advancements and Research in Engineering Computations, 9(2), 53–60. Retrieved from