Enhancing Smart Healthcare Systems with Machine Learning: A Cardiovascular Disease Prevention

Cardiovascular diseases (CVDs) remain the world’s leading causes of mortality despite advancements in healthcare. Addressing this challenge, our research focuses on utilizing machine learning (ML) and predictive analytics to enhance the early detection and prevention of CVDs. We introduce a feedforward neural network combined with Bayesian Regularization to improve model generalization and prevent overfitting. Our results showcase a commendable accuracy in detecting early signs of CVD, facilitating timely and tailored healthcare interventions. We also explore the ethical dimensions of using predictive analytics in healthcare, particularly concerning data privacy and the equitable distribution of medical resources. Our research outcomes suggest significant ML potential in transforming preventive healthcare by predicting disease risks accurately and sustainably. This study demonstrates the effectiveness of advanced analytical techniques in healthcare and sets a foundation for future innovations in medical diagnostics.