| Abstrakt: |
Many people today struggle with mental health conditions like anxiety and depression. These problems can be resolved with targeted counseling. Depression can cause some people to experience sad thoughts, irregular sleeping habits, a sense of loss of control over their lives, a desire to isolate themselves from friends and family, tension, headaches, and suicidal thoughts. To determine the severity of anxiety and depression, proper screening and monitoring techniques are required. Humans can identify them through patient behaviors, but machines are now able to do it too with the help of Machine Learning (ML) techniques. These techniques help anticipate patients' levels of anxiety and depression and also aid psychologists in treating their patients and in choosing the best course of action based on their level of disease. This paper reviews the studies published between 2010 and 2022. Some keywords such as Machine Learning, anxiety, and depression were used for collection purpose and tools such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Nearest Neighbor, Random Forest, Decision Tree and Naive Bayes were used for analysis, besides deep learning and Machine Learning. SVM and CNN algorithms outperformed other algorithms in terms of accuracy. This study highlights the most recent developments in the field. [ABSTRACT FROM AUTHOR] |
