Harnessing Deep Learning for Cardiovascular Risk: A Neural Network Revolution

Cardiac disorders are elevating reason for demise worldwide, claiming millions of lives annually. Early risk assessment and prevention are vital, as CVD is often linked to modifiable lifestyle factors. Leveraging a comprehensive dataset from Kaggle, featuring 12 key attributes, we developed an Artificial Neural Network (ANN) for binary classification to predict CVD risk. Our model, consisting of 6 hidden layers with 512-16 neurons, noted 82% result using the ADAM optimizer and binary cross-entropy loss. indicating a high degree of predictive capability. The ROC curve demonstrated high sensitivity and specificity, while the Loss vs Accuracy graph showed consistent improvements in both learning and validation accuracy. Our ANN model draws precious enlightenment for early CVD risk assessment, enabling potential interventions to reduce mortality rates. This deliberation showcases the inherent of Neural approach in tackling the growing universal epidemic of CVD. The use of neural networks in this context highlights the potential of neural learning approaches in tackling complex healthcare problems.