E-Healthcare Data Warehouse Design and Data Mining Using ML Approach

Abstract

This paper conducted a thorough analysis of 5G-enabled smart electronic healthcare (e-healthcare) solutions for the Internet of Things (IoT) cloud data warehouse system design, highlighting the need for medical smart devices IoT connectivity solutions the current digital society must address. The study highlighted requirements for the effective implementation of smart e-healthcare systems for particular 5G scenarios. The study found that bimodal sensor access using an artificial neural network (ANN) computer algorithm can be used to execute data mining techniques and their applications in smart e-healthcare services for risk management, fraud detection, security and identity authentication can be realized. In order to meet the demand for user-oriented medical services reform, the bimodal sensor electronic health (e-health) data mining system is required to enhance the phenomenon for information management between healthcare consultants and patients. The creation of an e-health data warehouse will make it possible for future medical smart devices to communicate with one another and to aggregate records from various heterogeneous database sources in order to meet the diversity requirements of medical and e-health management, which will hasten and reinforce the advancement of health information technology in the medical field and healthcare organizations. In the current study, distributed cloud IoT data warehouse server systems were created and connected through 5G radio frequency access network (RAN) to enable healthcare grid data warehouse information technology(IT) infrastructure synchronization, resource allocation and service-level conformity. The integration of a cloud-based IoT data warehousing platform was intended to facilitate global device connectivity management for e-healthcare administration, advancing innovative approaches to the creation and sharing of healthcare and medical data mining intelligence.