Reinforcement Learning Optimizing Some to type Based Predictive Models for Osteochondrosis in Males

Abstract

This paper brings a new Reinforcement Learning Optimizing Somatotype-Based Predictive Model to evaluating the risk of osteochondrosis among males. The presented framework incorporates the Z-Score Standardization of features to provide the same level of scaling and the Recursive Feature Elimination used to select the most important anthropometric and biomechanical predictors. One of the main predictive engines is an LGBM classifier, which was trained and deployed on PyTorch Lightning and improved through reinforcement learning to dynamically adjust model parameters and decision limits, and thus to provide greater adaptability. The system performed better, with high accuracy and recall and AUC values as opposed to the supervised traditional models. The model has the advantage of offering personalized risk prediction by taking somatotype-specific variations into account as well as being strong in real-time health monitoring cases. The findings point to the opportunities of the reinforcement learning-based optimization in the area of improvement of precision diagnostics of spinal health contributing to proactive clinical decision-making and preventive care strategies tailored to the individual.

Ähnliche Arbeiten