Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey

Abstract

Depression, a pervasive mental health disorder, has substantial impacts on both individuals and society. The conventional approach to predicting depression necessitates substantial collaboration between health care professionals and patients, leaving room for the influence of subjective factors. Consequently, it is imperative to develop a more efficient and accessible prediction methodology for depression. In recent years, numerous investigations have delved into depression prediction techniques, employing diverse data modalities and yielding notable advancements. Given the rapid progression of this domain, the present article comprehensively reviews major breakthroughs in depression prediction, encompassing multiple data modalities such as electrophysiological signals, brain imaging, audiovisual data, and text. By integrating depression prediction methods from various data modalities, it offers a comparative assessment of their advantages and limitations, providing a well-rounded perspective on how different modalities can complement each other for more accurate and holistic depression prediction. The survey begins by examining commonly used datasets, evaluation metrics, and methodological frameworks. For each data modality, it systematically analyzes traditional machine learning methods alongside the increasingly prevalent deep learning approaches, providing a comparative assessment of detection frameworks, feature representations, context modeling, and training strategies. Finally, the survey culminates with the identification of prospective avenues that warrant further exploration. It provides researchers with valuable insights and practical guidance to advance the field of depression prediction.