How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review.

Undergraduate students are often impacted by depression, anxiety, and stress. In this context, machine learning may support mental health assessment. Based on the following research question: "How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students?", we aimed to evaluate the performance of these models. PubMed, Embase, PsycINFO, and Web of Science databases were searched, aiming at studies meeting the following criteria: publication in English; targeting undergraduate university students; empirical studies; having been published in a scientific journal; and predicting anxiety, depression, or stress outcomes via machine learning. The certainty of evidence was analyzed using the GRADE. As of January 2024, 2,304 articles were found, and 48 studies met the inclusion criteria. Different types of data were identified, including behavioral, physiological, internet usage, neurocerebral, blood markers, mixed data, as well as demographic and mobility data. Among the 33 studies that provided accuracy assessment, 30 reported values that exceeded 70%. Accuracy in detecting stress ranged from 63% to 100%, anxiety from 53.69% to 97.9%, and depression from 73.5% to 99.1%. Although most models present adequate performance, it should be noted that 47 of them only performed internal validation, which may overstate the performance data. Moreover, the GRADE checklist suggested that the quality of the evidence was very low. These findings indicate that machine learning algorithms hold promise in Public Health; however, it is crucial to scrutinize their practical applicability. Further studies should invest mainly in external validation of the machine learning models.