Enhancing AI Adoption in Healthcare: A Data Strategy for Improved Heart Disease Prediction Accuracy Through Deep Learning Techniques

Abstract

This paper presents the development of an artificial neural network (ANN) for the prediction of heart disease, along with a comprehensive data strategy aimed at improving the adoption of artificial intelligence (AI) in healthcare. The neural network architecture is carefully designed according to the dimensions of the data, transfer learning methods are used to increase generalizabil1ity, and hyperparameters are optimized to achieve high predictive accuracy. To address the challenges related to AI adoption in healthcare, a robust data strategy is devised, focusing on data quality, privacy, security, and regulatory compliance. The strategy incorporates comprehensive data governance frameworks, secure data sharing protocols, and privacy-preserving techniques to facilitate the responsible and ethical utilization of sensitive medical information. Furthermore, strategies for ensuring interoperability and scalability of AI systems within existing healthcare infrastructure are explored.