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