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.