Single- and Multi-label Prediction of Burden on Families of Schizophrenia Patients

Whereas there exist questionnaires used to measure the level of anxiety or depression in caregivers of schizophrenia patients, sometimes these symptoms take too long to be detected and the treatment needed is more difficult than it would have been if the burden had been detected at an earlier stage. In this paper we propose the use of automatic classification techniques to predict the output of such questionnaires (Hamilton and ECFOS-II), making it possible to anticipate an appropriate treatment or advice for the family caregivers from Primary Care consultations. In particular, we apply standard (one class variable) and multi-dimensional classification approaches to predict caregiver anxiety, depression and answers to questionnaires. Our study has been carried out with a dataset containing data from 180 schizophrenia patients and their caregivers, and the results are very promising, obtaining accuracies of approximately 96%.