Emotional Disorders Detection Using Machine Learning Algorithm
Keywords:
Anxiety, Classification, Depression, Mental Health, Supervised machine learningAbstract
Emotional disorders, namely anxiety and depression are the most debilitating mental illness. Early detection will minimize risks of developing complex disorders and suicidality. This study aimed to build and evaluate machine learning classification models in screening for anxiety, depression, and healthy control. Supervised machine learning algorithms including Random Forest, Artificial Neural Network, Support Vector Machine, and Naive Bayes were applied and compared to build superior models. The best algorithm for multiclass classification was Artificial Neural Network with an F1-score of 0.97. Additionally, for binary classification, the Support Vector Machine yielded the highest performance for both the 'Depression and Anxiety' class (F1-score: 0.99) and the 'Depression' class (F1-score: 0.98). For the 'Anxiety' class, the Artificial Neural Network exhibited the best performance with an F1-score of 0.99, while the Random Forest algorithm achieved the highest F1-score of 0.98 for the 'Healthy' class. These findings hold potential in assisting clinicians by providing more efficient treatment strategies.
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