AI-Driven Predictive Analytics Framework for Secure and Intelligent Healthcare Systems

Abstract

Healthcare’s​‍​‌‍​‍‌ digital transformation greatly accelerated and opened up a new field of intelligent possibilities but at the same time introduced new security challenges. Nowadays, healthcare entities are very data-intensive, with the data influx coming from EHRs, medical devices, imaging systems, and applications. These huge data volumes make it difficult to accurately predict, decide timely and protect the data. The main thing that we are presenting here is an AI-driven predictive analytics framework that can provide intelligent insights while at the same time embedding security and privacy controls directly into the analytical lifecycle. The framework we put forward links machine learning models for risk prediction, anomaly detection, and outcome forecasting with security-aware data pipelines that use encryption, access control, auditability, and threat intelligence. By combining predictive intelligence, secure data handling, and explainable AI mechanisms, the framework satisfies both clinical trust and regulatory compliance. A healthcare real-life case study is used to illustrate the framework capabilities; the main focus is on early risk detection and secure data processing in the hospital setting. The results indicate that predictive accuracy, lower incident response time, and greater data access and model behavior visibility have been achieved without a drop in system performance. Apart from the technical advantages, this paper highlights that integrating security into predictive analytics can improve security posture and increase clinicians' trust in AI-assisted decision-making. One of the major contributions made by this work is the setting of a single intelligence and security viewpoint in healthcare analytics along with the development of a practical framework that can be adapted to various healthcare settings. In addition, the article gives practical tips to clinicians who want to implement AI in a responsible way. Altogether, our approach illustrates that predictive intelligence and strong security are not two opposing goals, but rather they are two interdependent pillars that underlie trustworthy, intelligent healthcare ​‍​‌‍​‍‌systems.