A robust framework for handling health care information based on machine learning and big data engineering techniques

Abstract

ABSTRACT Confidentiality and security breaches remain significant concerns for electronic healthcare systems even though many relevant rules, principles, and compliance standards are in place to protect health information. It is now essential to use strategies revolving around big data to enhance the dependability of healthcare delivery due to the ever-increasing number of data generated within the healthcare sector. Even though big data processing methods and platforms have been incorporated into the data management designs for medical systems, these designs have trouble addressing urgent situations. Today, it is difficult to predict how big data and ML will affect the healthcare sectors. As a result, a clinical healthcare data warehouse environment utilizing big data analytics and ML is provided in this analysis. Quick digital access to all types of vital data, including patient histories, scan records, insurance claims, and payment history, is guaranteed by healthcare data warehouse systems. This clinical healthcare data warehouse environment can improve an individual's quality of care and monitor the patient's health status in real-time by using ML algorithms and Big data analytics. The effectiveness of the ML technique's performance is measured in terms of accuracy, specificity, sensitivity, precision, recall and Receiver Operating Characteristics (RoC).