A Comprehensive Study on Healthcare Datasets Using AI Techniques

Due to greater accessibility, healthcare databases have grown over the years. In this paper, we practice locating and associating data points or observations that pertain similar entities across several datasets in public healthcare. Based on methods proposed study, all sources are allocated using AI-based approaches consider non-unique features calculate similarity indices. Critical components discussed include accuracy assessment, blocking criteria, linkage processes. Accurate measurements develop for manually evaluating validating matched pairs purify connecting parameters boost process efficacy. This study aims assess raise standard of aid doctors’ comprehension patients’ physical characteristics by NARX detect errors machine learning models decision-making process. Consequently, our findings mortality rate patients with COVID-19 revealed a gender bias: female 15.91% male 22.73%. We also found bias mild symptoms such as shortness breath: 31.82% 32.87%. With congestive heart disease symptoms, was follows: 5.07% 7.58%. Finally, typical overall both males females 13.2%.