A Survey on Health Care and Expert System

Abstract

Since 2010, big data has been more prevalent in healthcare due to three primary factors, including the vast amount of data available, the rising expense of healthcare, and a focus on individualized health-care services. Data that is too large or complicated for traditional data processing methods to process is referred to as "Big Data" in healthcare. A few examples of large-scale data sources in healthcare are the Internet of Things (IoT) and Electronic Medical Records (EMRs/EHRs), which contain patient medical history and diagnose information, medication regimens and treatment plans for a patient's condition as well as allergy information and results from laboratory and test tests, genomic sequencing, medical imaging and other clinical data sources. A variety of machine learning algorithms were used to analyze healthcare data in this study, and the results were discussed. Determining how to deal with huge data as well as the apps that use it. Using machine learning techniques and the necessity to handle and utilize massive data from a fresh perspective is the focus of the paper.