Machine Learning Algorithms for Depression: Diagnosis, Insights, and Research Directions

Over the years, stress, anxiety, and modern-day fast-paced lifestyles have had immense psychological effects on people's minds worldwide. The global technological development in healthcare digitizes the scopious data, enabling the map of the various forms of human biology more accurately than traditional measuring techniques. Machine learning (ML) has been accredited as an efficient approach for analyzing the massive amount of data in the healthcare domain. ML methodologies are being utilized in mental health to predict the probabilities of mental disorders and, therefore, execute potential treatment outcomes. This review paper enlists different machine learning algorithms used to detect and diagnose depression. The ML-based depression detection algorithms are categorized into three classes, classification, deep learning, and ensemble. A general model for depression diagnosis involving data extraction, pre-processing, training ML classifier, detection classification, and performance evaluation is presented. Moreover, it presents an overview to identify the objectives and limitations of different research studies presented in the domain of depression detection. Furthermore, it discussed future research possibilities in the field of depression diagnosis.

Automated summary

Stress, anxiety, and fast-paced lifestyles have significantly affected people's mental health worldwide. Machine learning is being utilized in the field of mental health to predict and treat depression. This review paper presents various machine learning algorithms used for depression detection and diagnosis, along with a general model for depression diagnosis.