Ischemic Heart Disease Prognosis: A Hybrid Residual Attention-Enhanced LSTM Model

Abstract

Well-timed prediction and an accurate diagnosis of Ischemic Heart disease (IHD) can reduce the risk of death, whereas an inaccurate diagnosis can prove fatal. So, there is a need to develop an optimal heart disease prediction model to avoid inaccurate ischemic heart disease diagnosis and further treatment. Recently, researchers have developed several deep learning techniques that take input from medical practitioners, automatically find hidden patterns in enormous volumes of data, and predict heart diseases without human intervention. Further, the deep learning model can help doctors to classify the severity of heart disease and choose appropriate treatment accordingly. These deep learning models can be improved to achieve greater accuracy and stability. Creating a hybrid model that combines attention residual learning with a Long Short-Term Memory (LSTM) is one method to prove it. Our suggested Hybrid Residual Attention-Enhanced LSTM (HRAE-LSTM) approach improves accuracy and stability by combining attention residual learning with an LSTM. For evaluating the effectiveness of the proposed HRAE-LSTM model, realistic datasets of 303 instances from the heart disease dataset (UCI), were used. The proposed HRAE-LSTM outperforms existing cardiac disease prediction systems by 97.7 % with the UCI dataset, respectively.