A Comparative Study of Machine Learning Algorithms in Predicting Mental Disorders

The study reviews the application of machine learning (ML) algorithms in predicting various mental disorders, highlighting their potential in real-time prediction. It discusses the use of different ML models, such as Adaboost, which predicted depression with high accuracy and 93.6% specificity, and XGBoost for post-stroke depression. Additionally, RNN was used to predict depression from EEG brain wave data, while SVM was explored for androgenic alopecia, and the RF model was evaluated for anxiety. The study emphasizes the growing role of ML in the medical field, particularly in diagnosing and predicting mental health conditions.