Comparing the Performance of Machine Learning Algorithms in the Automatic Classification of Psychotherapeutic Interactions in Avatar Therapy

Abstract

(1) Background: Avatar Therapy (AT) is currently being studied to help patients suffering from treatment-resistant schizophrenia. Facilitating annotations of immersive verbatims in AT by using classification algorithms could be an interesting avenue to reduce the time and cost of conducting such analysis and adding objective quantitative data in the classification of the different interactions taking place during the therapy. The aim of this study is to compare the performance of machine learning algorithms in the automatic annotation of immersive session verbatims of AT. (2) Methods: Five machine learning algorithms were implemented over a dataset as per the Scikit-Learn library: Support vector classifier, Linear support vector classifier, Multinomial Naïve Bayes, Decision Tree, and Multi-layer perceptron classifier. The dataset consisted of the 27 different types of interactions taking place in AT for the Avatar and the patient for 35 patients who underwent eight immersive sessions as part of their treatment in AT. (3) Results: The Linear SVC performed best over the dataset as compared with the other algorithms with the highest accuracy score, recall score, and F1-Score. The regular SVC performed best for precision. (4) Conclusions: This study presented an objective method for classifying textual interactions based on immersive session verbatims and gave a first comparison of multiple machine learning algorithms on AT.