  The combination of big data and deep learning is a world-shattering
technology that can greatly impact any objective if used properly. With the
availability of a large volume of health care 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 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 predict the future trend of heart failure based on that
health record. 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). The patients consisted of
105 women and 194 men and their ages ranged from 40 and 95 years old. The
dataset contains 13 features, which report clinical, body, and lifestyle
information responsible for heart failure. We have found an increasing trend in
our analysis which will contribute to advancing the knowledge in the field of
heart stroke prediction.
