Abstract:
"The research focuses on a problem in healthcare sectors regarding privacy and security issues.
Some of the identified problem include; insecurities such as unauthorized persons accessing the
patients’ room, data leakage through cybercrime, falls which can be prevented through digital
monitoring and immediate reporting, and accessing patients’ data from physical areas such as in
the registration office, and waiting room. Problems with existing models were discovered for
example they are; costly, require training to use, occupy much space in android and windows
device, slow down the device with low storage and RAM capacity, and high maintenance costs.
Suggestions were made that a machine learning model can increase monitoring healthcare’s’
performance which would thus, increase privacy and enhance security on patient’s data and
wellbeing in the facility. Data was collected using case studies approach whereby, interviews were
analyzed.
The results indicate that many healthcare organizations have been experiencing minimal privacy
and security has been a significant issue which in some cases, worsen the patients’ condition.
Where monitoring in healthcare would be increased, there would be reduction in unauthorized
persons accessing the patients’ room, in addition, a monitoring tool can monitor patients’
movement and create an alert to the management in time. In the study, CNN was not applied,
however, to solve the problem a machine learning tool which would resemble datix model was
suggested. The tool is an improved version of the existing machine learning tools. Confusion
matrix was applied to make predictions on the accuracy and precision with the tool to test its
reliability, functionality, and to test whether it can help healthcare organizations improve on
privacy and security reporting in the facility."
