Department of Computer Science and Engineering, Saveetha school of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
As in heart disease patients in biomarkers such as heart rate, ECG (electrocardiogram), pulse rate slow due to blood pressure is essential to get to know about heart disease. Deep learning model for HD diagnosis as wearable sensors collecting and applied as a inputs for measureable. Data gathering and in balancing are observing by the model accuracy. In this current study DL framework such as CNN, BiLSTM, Bi,LRU model used with GAI hybridization technique. In this current study computed for the results on using the different machine learning techniques for also drug recovery in heart disease through deep learning. BiLSTM is a bidirectional model which s used to generate the better results through long short term memory. BILSTM-GAI & BILRU-GAI model hybridization technique to evaluate the framework by generative model. The deep learning model gives the better accuracy as in terms of prediction of heart disease. The generative artificial intelligence is computing on the patient attributes. Heart disease is a major disease at an early stages and it is very difficult to detect and diagnose by physicians. This model is train and test to diagnose the HD. The Cleveland dataset has taken for detects and diagnoses heart disease.
Keywords
Bidirectional long short term memory, generative artificial intelligence, heart disease, Convolutional neural networks etc.
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Shailee Lohmor Choudhary
Department of Artificial Intelligence and Machine Learning, New Delhi Institute of Management, Tughlakabad Institutional Area, Delhi, India.
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Cite this article
Shailee Lohmor Choudhary, Ritu Aggarwal, Rinku Sharma Dixit, Baskar Duraisamy, Divya Sundar V S and Sulakshana Bhausaheb Mane, “HDLHDGAI: Hybridization of Deep Learning Model for Heart Disease Prediction using Generative Artificial Intelligence”, Journal of Machine and Computing. doi: 10.53759/7669/jmc202505008.
