A Review of Prediction of Depression Using Machine Learning Algorithms
Depression is a widespread and debilitating mental health disorder that significantly affects individuals’ emotional, cognitive, and social functioning. Traditional diagnostic methods often rely on subjective assessments, which can delay timely detection and intervention. Recent advancements in artificial intelligence, particularly machine learning, have created opportunities to enhance depression prediction and diagnosis by leveraging diverse data sources. This study aims to review existing research on the application of machine learning algorithms for predicting depression, highlighting their strengths, limitations, and potential for improving mental health care. A review approach was employed, producing literature published between 2019 and 2025 from reputable databases such as IEEE Xplore, PubMed, Scopus, Web of Science, and Google Scholar. Eligible studies applied machine learning or deep learning techniques to structured and unstructured data sources, including clinical records, social media content, multimodal signals, and wearable devices, and reported performance metrics such as accuracy, precision, recall, and F1-score. The review reveals that machine learning models, particularly ensemble methods, deep learning architectures, and hybrid frameworks, demonstrate strong predictive performance across diverse datasets, achieving accuracies above 90% in several cases. However, challenges such as limited data availability, class imbalance, lack of interpretability, ethical concerns around privacy and bias, and difficulties in clinical integration persist. The findings underscore the need for explainable, ethically responsible, and culturally adaptable predictive systems that can be seamlessly embedded into healthcare workflows.
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