Diagnosing Cataracts in the Digital Age: A Survey on AI, Metaverse, and Digital Twin Applications.

Purpose: The study explores the evolving landscape of cataract diagnosis, focusing on both traditional methods and innovative technological integrations. It aims to address challenges with subjectivity in traditional cataract grading and to evaluate how new technologies can enhance diagnostic accuracy and accessibility.

Methods: The research introduces and examines the use of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in automating and improving cataract screening processes.

Examining different cost ratio frameworks for decision rule machine learning algorithms in diagnostic application.

Background: Artificial Intelligence (AI) plays a pivotal role in the diagnosis of health conditions ranging from general well-being to critical health issues. In the realm of health diagnostics, an often overlooked but critical aspect is the consideration of cost-sensitive learning, a facet that this study prioritizes over the non-invasive nature of the diagnostic process whereas the other standard metrics such as accuracy and sensitivity reflect weakness in error profile.

Objective: This research aims to investigate the total cost of misclassification (Total Cost) by decision rule Machine Learning (ML) algorithms implemented in Java platforms such as DecisionTable, JRip, OneR, and PART.