Applying Bayesian Ridge Regression AI Modeling in Virus Severity Prediction

TL;DR

This paper reviews Bayesian Ridge Regression for virus severity prediction, highlighting its potential for healthcare, its accuracy, and the importance of data organization for improved performance.

Contribution

It provides an evaluation of Bayesian Ridge Regression in virus severity prediction and discusses its strengths, weaknesses, and potential for healthcare applications.

Findings

Promising accuracy results with room for improvement

Severity index offers broad overview of patient needs

Data organization impacts model performance

Abstract

Artificial intelligence (AI) is a powerful tool for reshaping healthcare systems. In healthcare, AI is invaluable for its capacity to manage vast amounts of data, which can lead to more accurate and speedy diagnoses, ultimately easing the workload on healthcare professionals. As a result, AI has proven itself to be a power tool across various industries, simplifying complex tasks and pattern recognition that would otherwise be overwhelming for humans or traditional computer algorithms. In this paper, we review the strengths and weaknesses of Bayesian Ridge Regression, an AI model that can be used to bring cutting edge virus analysis to healthcare professionals around the world. The model's accuracy assessment revealed promising results, with room for improvement primarily related to data organization. In addition, the severity index serves as a valuable tool to gain a broad overview of patient care needs, aligning with healthcare professionals' preference for broader categorizations.