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Semi-supervised vs. supervised learning for mental health monitoring: A case study on bipolar disorder

Casalino Gabriella, Castellano Giovanna, Hryniewicz Olgierd, Leite Daniel, Opara Karol, Radziszewska Weronika, Kaczmarek-Majer Katarzyna

International Journal of Applied Mathematics and Computer Science

2023

Vol. 33, no. 3

419--428

Acoustic features of speech are promising as objective markers for mental health monitoring. Specialized smartphone apps can gather such acoustic data without disrupting the daily activities of patients. Nonetheless, the psychiatric assessment of the patient’s mental state is typically a sporadic occurrence that takes place every few months. Consequently, only a slight fraction of the acoustic data is labeled and applicable for supervised learning. The majority of the related work on mental health monitoring limits the considerations only to labeled data using a predefined ground-truth period. On the other hand, semi-supervised methods make it possible to utilize the entire dataset, exploiting the regularities in the unlabeled portion of the data to improve the predictive power of a model. To assess the applicability of semi-supervised learning approaches, we discuss selected state-of-the-art semi-supervised classifiers, namely, label spreading, label propagation, a semi-supervised support vector machine, and the self training classifier. We use real-world data obtained from a bipolar disorder patient to compare the performance of the different methods with that of baseline supervised learning methods. The experiment shows that semi-supervised learning algorithms can outperform supervised algorithms in predicting bipolar disorder episodes.

Impact of clustering unlabeled data on classification: case study in bipolar disorder

Kamińska Olga, Kaczmarek-Majer Katarzyna, Hryniewicz Olgierd

Annals of Computer Science and Information Systems

2022

Vol. 30

931--934

Currently, it is possible to collect large amount of data from sensors. At the same time, data are often only partially labeled. For example, in the context of smartphone-based monitoring of mental state, there are much more data collected from smartphones than those collected from psychiatrists about the mental state. The approach presented in this paper is designed to examine if unlabeled data can improve the accuracy of classification task in the considered case study of classifying a patient's state.First, unlabeled data are represented by clusters membership through Fuzzy C-means algorithm which corresponds to the uncertainty of the patient's condition in this disease. Secondly, the classification is perform using two well-known algorithms, Random Forest and SVM. The obtained results indicate a minimal improvement in the quality of classification thanks to the use of membership in clusters. These results are promising and also interpretable.