Personalized Characterization of Emotional States in Patients with Bipolar Disorder

Pavel Llamocca, Victoria López, Matilde Santos and Milena Čukić
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Pavel Llamocca: Computer Sciences Faculty, Complutense University of Madrid, 28040 Madrid, Spain
Victoria López: Quantitative Methods Department, Cunef University, 28040 Madrid, Spain
Matilde Santos: Institute of Knowledge Technology, Complutense University of Madrid, 28040 Madrid, Spain
Milena Čukić: AHTI, Amsterdam Health and Technology Institute, HealthInc, 1062KS Amsterdam, The Netherlands

Mathematics, 2021, vol. 9, issue 11, 1-18

Abstract: There is strong clinical evidence from the current literature that certain psychological and physiological indicators are closely related to mood changes. However, patients with mental illnesses who present similar behavior may be diagnosed differently, which is why a personalized study of each patient is necessary. Following previous promising results in the detection of depression, in this work, supervised machine learning (ML) algorithms were applied to classify the different states of patients diagnosed with bipolar depressive disorder (BDD). The purpose of this study was to provide relevant information to medical staff and patients’ relatives in order to help them make decisions that may lead to a better management of the disease. The information used was collected from BDD patients through wearable devices (smartwatches), daily self-reports, and medical observation at regular appointments. The variables were processed and then statistical techniques of data analysis, normalization, noise reduction, and feature selection were applied. An individual analysis of each patient was carried out. Random Forest, Decision Trees, Logistic Regression, and Support Vector Machine algorithms were applied with different configurations. The results allowed us to draw some conclusions. Random Forest achieved the most accurate classification, but none of the applied models were the best technique for all patients. Besides, the classification using only selected variables produced better results than using all available information, though the amount and source of the relevant variables differed for each patient. Finally, the smartwatch was the most relevant source of information.

Keywords: decision making; machine learning; classification; bipolar disorder; mental healthcare (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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