Ethical Considerations in Utilizing Machine Learning for Depression and Anxiety Detection in College Students

Abstract

Depression and Anxiety are two of the most common mental disorders that are happening nowadays worldwide. This systematic review paper investigates the application of neural network-based machine learning techniques in assessing, identifying, and diagnosing depression and anxiety, which are two worldwide mental health problems. These approaches can range from many different types of neural network architectures like classical supervised learning methods to unsupervised approaches with recent deep models; if a systematic literature review of studies conducted in the last five years is carried out using databases like Science Direct and PubMed, these approaches can show promising results in tasks like clinical data analysis, biomarker identification, and personalized treatment plan creation. This makes it possible to explain the 91.08% accuracy ratio that he reported in his study, which gave rise to a comprehensive confusion matrix and analysis of the classification report for the employed neural network model (Level 6). There are still difficulties in correctly identifying cases of depression (class 1) despite relatively high recall and precision in non-depressive cases (class 0). This raises possible areas for improvement, particularly with the current class imbalances. This review paper can serve as a valuable source of information for all interested in neural network-based machine learning and how it may be used to address depression and anxiety, thereby providing insight into the future that will likely change mental treatment models.