Early Detection of Cardiovascular Diseases through Convolutional Neural Networks and Long Short-Term Memory Models

Abstract

This research paper explores the integration of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models for the early detection of cardiovascular diseases (CVDs), a leading cause of mortality worldwide. The study aims to enhance diagnostic accuracy by leveraging the strengths of CNNs in feature extraction and LSTMs in temporal sequence learning. We curated a robust dataset comprising thousands of annotated electrocardiogram (ECG) recordings, representing diverse cardiovascular conditions. The proposed hybrid model initially employs a CNN to extract hierarchical features from ECG signal images, which are then fed into an LSTM network to capture temporal dependencies crucial for precise diagnosis. Experimental results demonstrate the model's superior performance, with accuracy rates surpassing conventional methods by 15%, achieving an F1-score of 0.92 and a recall of 0.89 across a wide range of CVDs. The model's real-time processing capability enables its potential deployment in wearable technology, facilitating proactive patient monitoring and timely medical interventions. This study underscores the transformative potential of combining deep learning architectures in the medical domain, paving the way for advanced, non-invasive healthcare solutions. Further research is recommended to validate these findings across larger, more diverse populations and to explore the integration of additional physiological data.