Semi-supervised Learning for a Healthcare Support Method using Markov Decision Processes

Abstract

Markov decision processes are applied to a healthcare support method in previous research. In the previous research the true parameters of Markov decision processes are known. In this research we propose a semi-supervised learning method for the healthcare support method under the condition that the true parameters of Markov decision processes are unknown. Learning data consist of complete data and incomplete data. In the proposed method EM(expectation-maximization) algorithm is used. The effectiveness of the proposed method is shown by some simulations. The result shows that the learning accuracy becomes higher as the learning data becomes bigger.