Analysis and prediction of heart stroke from ejection fraction and serum creatinine using LSTM deep learning approach

TL;DR

This paper presents an LSTM-based deep learning model that predicts heart failure by analyzing electronic health records, focusing on features like ejection fraction and serum creatinine, to improve early detection.

Contribution

The study introduces a novel LSTM approach utilizing electronic health records for heart failure prediction, addressing limitations of previous models like SVM that did not incorporate objective factors.

Findings

LSTM achieved promising prediction accuracy on heart failure data.

Identified key health features influencing heart failure risk.

Analyzed a dataset of 299 patients to validate the model.

Abstract

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.