RNN-GRU hybrid deep learning framework for heart disease prediction

Abstract

This research presents a novel hybrid deep learning approach for heart disease prediction, incorporating a Recurrent Neural Network (RNN) with multiple Gated Recurrent Units (GRU) and the Adam optimizer. The performance of the proposed model is evaluated on the IEEE Dataport heart disease dataset, achieving an accuracy of 92%. The primary focus of this research is to investigate the impact of data pre-processing, particularly outlier detection, on the accuracy of the hybrid deep learning model. The comparative analysis demonstrates that without outlier detection, the model achieves an accuracy of 89%, while incorporating outlier detection results in an increase in accuracy of 3%, reaching 92%. The study involves implementing a hybrid RNN-GRU model on a pre-processed dataset. Research tracks the accuracy and loss for each epoch throughout the training and testing phases. The results highlight the usefulness of the model in predicting heart disease and stress the importance of data pre-processing in improving its performance.