Cloud-Enabled Regression Model for Enhanced Patient Authentication Systems

Protecting private health information and facilitating easy access to healthcare services depends on reliable patient authentication. Improving patient authentication systems via cloud computing and Artificial Intelligence (AI) approaches is the main topic of this paper. It uses linear regression (LR) models to provide a scalable solution that uses big datasets to enhance authentication efficiency and accuracy by using cloud-based platforms. LR examines several authentication elements to predict and validate patient identities, such as user behavior patterns and biometric data. The proposed solution aims to reduce authentication failures and mitigate possible security concerns while preserving a user-friendly experience. With better performance in terms of authentication accuracy and system responsiveness, experimental findings reveal that the cloud enabled strategy is beneficial. This system provides valuable information for future research on strong patient authentication systems and shows how cloud computing and AI may work together to improve healthcare security.