Predictive Analysis-Based AI-Driven Data Security Authentication and Authorization for Medical Warehousing Mechanisms

Abstract

Maintaining data security is important for several reasons, as it shields patients, first and foremost, from fraud, identity theft, and prejudice due to their medical history. This paper presents a comprehensive framework for enhancing data security authentication and authorization within medical warehousing mechanisms in the context of e-commerce, leveraging predictive analysis, artificial intelligence (AI), and blockchain technology. In an era where the integrity and confidentiality of medical data are paramount, the proposed framework integrates advanced predictive analysis models and AI-driven authentication mechanisms with the immutable nature of blockchain to ensure robust security measures. The proposed solution presents an innovative approach to enhance data security authentication and authorization within medical warehousing mechanisms, leveraging predictive analysis and AI algorithms within the context of e-commerce-enabled blockchain. Specifically, the study focuses on employing Naive Bayes, LSTM, and XGBoost for predictive analysis to fortify security measures.