This paper introduces a reinforcement‑learning‑based framework that automatically selects classification algorithms to improve prediction of Primary Biliary Cirrhosis. Traditionally, choosing a suitable classifier requires extensive manual trials, which is time‑consuming and subjective. To address this, the authors devise a "Square Learning" (SL) scoring scheme that lets the RL agent evaluate classifiers on four common metrics—accuracy, recall, precision, and F1. Building on SL, they propose the "Fourth Degree Learning" algorithm, where the agent updates its policy based on the current classifier's performance and eventually converges to the optimal algorithm combination. Experiments on a public dataset show the accuracy rising from 63% to 98% compared with manual selection. The approach demonstrates the power of RL in meta‑learning and offers an automated, interpretable solution for model selection in medical diagnosis.
Review: The work creatively merges reinforcement learning with classifier selection, achieving a remarkable accuracy boost and suggesting broad applicability to other biomedical prediction tasks.