Prediction of Heart Disease Diagnosis Using Deep Learning Fusion Algorithm for Patient Monitoring System

Deep learning algorithms have become increasingly popular in the field of medical diagnosis due to their ability to identify complex patterns and correlations in medical data in an efficient manner. In this paper, we present a novel deep learning fusion algorithm for the diagnosis of heart disease. The proposed algorithm combines convolutional neural network (CNN) and long short-term memory (LSTM) neural networks to extract both spatial and temporal features from electrocardiogram (ECG) data. The algorithm is then used to analyse the ECG signals of patients in order to accurately diagnose a heart disease. The performance of the proposed algorithm is evaluated on the MIT-BIH Arrhythmia Database, where it achieved an accuracy of 95.5%. The results suggest that the proposed algorithm could be used for the early and accurate diagnosis of heart disease.