Predicting Potential Risk: Cerebral Stroke via Regret Minimization

Abstract

Objective. The data processing of medical test report has always been one of the important contents in biological information domain, especially the process of extracting the effective information from the report so as to assist doctors with the correct medical plan. Usual methods neglect the implicit relationship between features. More features are generally not a better choice because more noise is generated between feature combinations. We propose a practical feature selection strategy RMFS, which aims to select the optimal combination of features. Materials and Methods. Based on the above situation, in this paper, 64 features are extracted from a real medical test report dataset for stroke and feature selection is defined as a reinforcement learning problem to optimize the feature combination by minimizing regret. We select three current mainstream feature selection methods and conduct comparative experiments. Results. We processed and completed a dataset derived from real medical test reports of stroke. We redefine the feature selection problem as a reinforcement learning problem and propose an optimization strategy based on regret minimization and train weight parameters in a DQN network. Experimental results demonstrate that our method can identify feature combinations with higher prediction accuracy. Discussion. RMFS shows a strong robustness to the randomness of the environment and has high computational efficiency and accuracy. Compared with the previous feature selection methods, our method yields superior results. Conclusion. The experimental results demonstrate that our method can obtain a more accurate prediction rate under the same feature scale and we can achieve baseline performance with fewer features.