Brief Examination Of Cardiovascular Disease Using Deep Learning Recurrent Neural Network

Abstract

 Cardiovascular diseases (CVDs) represent one of the leading causes of morbidity and mortality across the globe, posing significant challenges to healthcare systems. Early and accurate detection of cardiovascular abnormalities is critical to improving survival rates and preventing severe complications. The proposed research focuses on a brief examination and predictive analysis of cardiovascular diseases using a Deep Learning-based Recurrent Neural Network (RNN) framework. The study aims to design and implement an intelligent system capable of analyzing complex, sequential medical data to predict potential cardiovascular risks with high accuracy. The proposed model leverages the strength of RNNs in capturing temporal dependencies and dynamic relationships within time-series health data such as electrocardiogram (ECG) signals, heart rate variability, blood pressure, cholesterol levels, and other clinical features. Data preprocessing techniques including normalization, noise removal, and feature extraction are applied to enhance data quality and improve model efficiency. The RNN is trained using optimized hyperparameters and validated through cross-validation methods to ensure generalization and robustness. Experimental evaluation is carried out using benchmark cardiovascular datasets to assess the performance of the proposed RNN model. The system’s predictive capabilities are compared with traditional machine learning algorithms such as Support Vector Machines (SVM), Decision Trees, and Logistic Regression. Results indicate that the proposed RNN model achieves superior accuracy, sensitivity, specificity, and F1-score, demonstrating its effectiveness in identifying early symptoms and progression patterns of cardiovascular diseases.