Hybrid CNN-LSTM Model for Cardiovascular Disease Prediction

Abstract

Cardiovascular Disease (CVD) remains one of the leading causes of mortality worldwide, necessitating early and accurate prediction methods for effective prevention and treatment. This paper presents a Hybrid CNN-LSTM model for Cardiovascular Disease Prediction that combines the spatial feature extraction capabilities of Convolutional Neural Networks with the temporal learning strengths of Long Short-Term Memory networks. The proposed framework processes clinical and time-series patient data to capture both local feature patterns and long-term dependencies. Data preprocessing techniques, including normalization and feature selection, are applied to enhance data quality and model performance. The hybrid architecture enables improved representation learning, leading to better prediction accuracy compared to traditional machine learning models. Experimental results demonstrate that the proposed model achieves superior performance in terms of accuracy, precision, and recall. Additionally, integration with prior research in ensemble learning and healthcare analytics strengthens the robustness and generalization capability of the system. The study highlights the effectiveness of hybrid deep learning approaches in developing reliable and scalable solutions for early cardiovascular disease prediction.