Cardiovascular Disease Prediction Using Hybrid CNN-LSTM Architecture in Deep Learning

Abstract

Accurate diagnosis of cardiovascular disease is still a difficult problem, as clinical parameters show complex interactions and are continuously varying over time, making their prediction with conventional methods difficult. Current statistical and shallow learning methods often utilize handcrafted features, which are not capable of fully leveraging the vast information of patient data stored in multiple formats. We propose a hybrid deep learning framework in this study, which learns discriminative clinical representations and health patterns over time together with Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Clinical observations first are standardized to ensure uniformity in data and then passed through convolutional layers to create informative feature representations which are then improved by recurrent memory units to model temporal relationships between patient features. The developed framework has been applied using TensorFlow and tested on a benchmark dataset of cardiovascular diseases in the same experimental setup with conventional machine learning classifiers and individual deep learning models. The proposed architecture achieved 93.6% classification accuracy, 92.8% precision, 93.1% recall, 92.9% F1 score and ROC-AUC of 0.96 all of which showed consistent improvements over Logistic Regression, Decision Tree, Support Vector Machine, stand-alone CNN and stand-alone LSTM models.