Prediction of schizophrenia from activity data using hidden Markov model parameters

Abstract

In this paper, we address the problem of predicting schizophrenia based on a persons measured motor activity over time. A key challenge to achieve this is how to extract features from the activity data that can efficiently separate schizophrenia patients from healthy subjects. To achieve this, we suggest to fit time dependent hidden Markov models with and without integrated covariates and letting the estimated model parameters represent our features. To further evaluate the efficiency of these features, we suggest to use them as features in a classification method (logistic regression) to separate schizophrenia patients from healthy subjects. The results show that the estimated hidden Markov model parameters are well-performing in predicting schizophrenia, and outperform features derived from other methods in the literature in terms of goodness-of-fit and classification performance.