Machine Learning based Network Security in Healthcare System

Abstract

The world is filled with exciting technologies and ideas; scientists build machines to avoid human intervention in completing work. It is highly challenging to complete the task without the Machine Learning (ML) Technology intervention. With the technological development, certain processes or consultations are performed with the aid of doctors available around the world. In this scenario, it could be noticed that health care is one of the world's expected domains that require the most incredible attention in data security while performing data transfer. Nodes in the network are considered based on the weakest link to overcome the cyber attacker's issues. Besides building the software for data storage, a better mechanism has to be incorporated to provide security to the stored data. This process is a delicate task for every network engineer. This paper will explain such concepts related to health prediction and health care by building the most robust network security systems. Finally, the discussion would cross over human-looping systems, which act as one of the common problems that are affected mentally for a person. According to the results, the suggested model achieved the accuracy of 98.89%, that is 4.76% greater than the previous model.