A Machine Learning Classification Model for Identifying College Students with Depression Based on Digital Phenotyping - Ribeiro, Evandro Y. A.; Garcia, Franco E.; Saud, Conrado dos S. Alves; Caseli, Helena de M.; Motti, Vivian G.; Bleicher, Taís; Neto, Jair B.; Frizzo, Heloisa C. Figueiredo; Martini, Larissa C.; Neris, Luciano de O.; Ara, Anderson; Valejo, Alan D. Baria; et al. (Journal of the Brazilian Computer Society, 2026)
Depression is a serious global mental health illness that causes significant suffering to the individual and social impairment in their lives. Compared to the general population, depression shows a higher prevalence among college students. With recent advancements in digital phenotyping data analysis to infer depressive symptoms, machine learning (ML) techniques have been increasingly employed to indicate behaviors related to potential depressive profiles (PDP). However, despite the growing body of work on ML usage to detect depression, few studies have focused on data preprocessing approaches to handle missing values in datasets that go beyond common data imputation. In this study, we conducted a series of experiments to evaluate the combination of data preprocessing methods and ML algorithms for effectively classifying PDP and non-PDP students using data from the Amive project. The primary challenges were implementing a data processing workflow to address missing values and class imbalance, common issues in digital phenotyping datasets, and selecting algorithms capable of handling such data. The experimental results showed promising outcomes, with individual classification models, including Random Forest, XGBoost, and SVM(rbf), achieving accuracies of 77%, 75%, and 76%, respectively. The best performance was obtained by training on datasets that went through outlier filtering, specifically removing rows with four or more missing values. This combination of data preprocessing approaches and ML algorithms resulted in a Random Forest classification model with the best performance ranging between 77% of accuracy and with mean errors metrics of AUC and MCC above 0.5.