Analysis and prediction of heart stroke using LSTM deep learning approach

Combining big data and deep learning is a world-shattering technology that can significantly impact any objective if appropriately used. With a large volume of available healthcare datasets and progressions in deep learning techniques, systems are now well-equipped to predict the future trend of any health problems. From the literature survey, we found that the SVM was used to predict the heart failure rate without relating objective factors. Utilizing the intensity of important historical information in electronic health records (EHR), we have built a smart and predictive model utilizing long short-term memory (LSTM) and incorporating correlation between clinical variables. Hence, the fundamental commitment of this work is to predict the failure of the heart using an LSTM based on the patient’s electronic medicinal information. We have analyzed a dataset containing the medical records of 299 heart failure patients collected at the Faisalabad Institute of Cardiology and the Allied Hospital in Faisalabad (Punjab, Pakistan). We have found an increasing trend in our analysis which will contribute to advancing the knowledge in the field of heart stroke prediction.