Using Regression Model Analysis for Forecasting the Likelihood of Particular Symptoms of COVID-19

Abstract

A certainty factor (CF) rule-based technique is frequently used by traditional expert systems (TES) in the medical industry to compute several symptoms and identify the inference solutions. The primary concern for this TES was predicting the likelihood of a particular ailment in the circumstances of new patients. Based on symptoms connected to clinical indicators in patients' diagnosis, CF is estimated. This TES probably won't be able to forecast unknown things, like the possibility of a particular ailment. Therefore, supervised learning techniques like linear regression can address this issue. We attempted to analyze the current COVID-19 TES by modeling the regression equation to forecast the chance of a particular disease that is COVID-like based on the CF value and the confidence level of the symptoms. To examine the most effective regression model to address the issue, we employed multi-linear regression (MLR) and multi-polynomial regression (MPR). The findings demonstrate that the MLR and MPR models are the most accurate regression models for estimating the chance of a disease associated with COVID-like symptoms. Our work built a basis for the creation of expert systems by concentrating more on MLES (machine learning expert systems) analytical techniques than TES.