A REVIEW AND COMPARATIVE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR DEPRESSION DETECTION

Abstract

Depression can be considered one of the most severe mental disorders which may negatively impact individuals' mood and productivity. Therefore, early diagnosis
of depression is required to prevent any further complications of this disease, such as suicide attempts. Owing to the growing interest in machine learning and deep
learning-based technologies, many automated approaches have been proposed in recent years aimed at determining whether an individual is suffering from
depression. These automated approaches employ different kinds of data, such as audio, video, and textual data. The primary purpose of the proposed research study
is to examine different approaches to the problem of detecting depression based on AI technologies during 2021-2025.
As far as potential ways of detecting depression, the following techniques may be applied: SVM (support vector machines), Naive Bayes, and Random forest,
which belong to the machine learning area. In addition to the above techniques, we may suggest applying deep learning neural networks such as CNN (convolution
neural networks), RNN (recurring neural networks), and LSTM (long short-term memory networks). What is more, we have the possibility of combining several
approaches together to achieve better results. Last but not least, we should mention natural language processing as another way of analyzing the information
obtained.In spite of all those advancements, many difficulties remain that need to be addressed, specifically concerning data privacy, class imbalance, absence of
labeled datasets, and the difficulty in explaining deep networks. Conclusion As can be seen from the above discussion, while deep learning and hybrid techniques
perform better than traditional approaches in terms of efficiency and scalability, much remains to be accomplished regarding explain ability and privacy.