CVNET - Cardiovascular Disease Detection using Deep Convolutional Neural Network

Cardiovascular diseases (CVDs) pose a serious threat to world health. This work uses a large dataset with clinical and demographic information to predict CVD risk using deep learning techniques. In order to enhance generalization and reduce overfitting, a Convolutional Neural Network (CNN) architecture was created, combining Batch Normalization and Dropout layers. A thorough assessment of the model’s performance was conducted using metrics including F1-score, accuracy, precision, and recall. With an astounding accuracy rate of 98.54%, the CVNET model outperformed more conventional deep learning techniques in terms of predictive power. These results highlight how deep learning models can improve early cardiovascular disease prediction, which permits prompt diagnosis and tailored treatment strategies. The commercialization of cutting-edge deep learning methods offers a viable strategy for reducing the prevalence of cardiovascular illnesses worldwide.