Machine Learning and Deep Learning Approaches for Mental Health Prediction: Applications and Challenges
Keywords:
Mental Health, Prediction, Support Vector Machine, Machine Learning and Deep LearningAbstract
In our fast-paced modern society, the prevalence of psychological health issues such as anxiety, depression, and stress has surged, affecting individuals across diverse cultures and communities. This paper explores the prediction of anxiety, depression, and stress levels using machine learning algorithms. Five different machine learning algorithms were employed to predict the severity of anxiety, depression, and stress across the sampled population. These algorithms were chosen for their high accuracy, making them particularly well-suited for predicting psychological problems. However, applying these algorithms made it apparent that class imbalances existed in the confusion matrix, necessitating the incorporation of the f1 score measure. Including the f1 score proved crucial in identifying the most accurate model among the five applied algorithms, ultimately revealing the Random Forest classifier as the most effective in predicting anxiety, depression, and stress levels. Moreover, the specificity parameter was examined, uncovering that the algorithms exhibited notable sensitivity to negative results. This study contributes to the growing body of research on utilizing machine learning in mental health prediction, offering valuable insights into the intricacies of class imbalances and the importance of performance metrics like the f1 score and specificity. The findings underscore the potential of machine learning algorithms, particularly the Random Forest classifier, in enhancing our understanding and prediction of psychological health issues in a diverse and dynamic population.
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