Enhanced LSTM for heart disease prediction in IoT-enabled smart healthcare systems

Abstract

Cardiac patients require prompt and effective treatment to prevent heart attacks through accurate prediction of heart disease. The prognosis of heart disease is complex and requires advanced knowledge and expertise. Healthcare systems are increasingly integrated with the internet of things (IoT) to collect data from sensors for diagnosing and predicting diseases. Current methods employ machine learning (ML) for these tasks, but they often fall short in creating an intelligent framework due to difficulties in handling high-dimensional data. A groundbreaking health system leverages IoT and an optimized long short-term memory (LSTM) algorithm, enhanced by the red deer (RD) algorithm, to accurately diagnose cardiac issues. Continuous monitoring of blood pressure and electrocardiograms (ECG) is conducted through heart monitor devices and smartwatches linked to patients. The gathered data is combined using a feature fusion approach, integrating electronic medical records (EMR) and sensor data for the extraction process. The RD-LSTM model classifies cardiac conditions as either normal or abnormal, and its performance is benchmarked against other deep-learning (DL) models. The RD-LSTM model showed better improvement in prediction accuracy over previous models.