AI-Driven Healthcare Data Management and Predictive Analytics using Cloud Computing and Secure Authentication

Authors

  • Vijai Anand Ramar Delta Dental Insurance Company, Georgia, USA Author

Keywords:

Artificial Intelligence, Cloud Computing, Recurrent Neural Networks, Data Preprocessing, Feature Extraction, Multi-factor Authentication, Healthcare Prediction

Abstract

This study presents the improvements in health service delivery and revenue management of health-related data, mainly relying on real-time predictive analysis, anomaly detection, and personalized care management. A system for data collection organized through structured methodology involves a variety of sources of health care data such as electronic health records (EHR), wearable sensors, and medical devices. It has the intricate processes through pre-processing, handling missing values, normalizing, and extracting features through the Fast Fourier Transform (FFT) technique. The pre-processed data have been secured while still permitting organization for further processing and analysis. Multi-factor authentication (MFA) was set up to safeguard patient information for confidentiality. Different performance metrics were involved in evaluating the models with the total accuracy of 96.44%, precision 95.80%, recall 94.95%, and F1 score 95.37% and validated the strength of predicting health outcomes. The current integrated frame thus provides an increasingly strong justification for predictive health-care initiatives and the study of artificial intelligence, cloud computing, RNNs, and security and protection measures in data storage systems.

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Published

2023-11-05

How to Cite

AI-Driven Healthcare Data Management and Predictive Analytics using Cloud Computing and Secure Authentication. (2023). INTERNATIONAL JOURNAL OF MANAGEMENT RESEARCH AND REVIEW, 13(4), 66-76. https://ijmrr.com/index.php/ijmrr/article/view/387