AI-Driven Personalization of E-Therapy Interventions for Anxiety, Stress, and Depression

This study explores the use of artificial intelligence (AI) to personalize e-therapy interventions for anxiety, stress, and depression. Leveraging machine learning models, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Decision Trees, we analyze a dataset of depression, anxiety, and stress scales responses to develop predictive models that tailor therapeutic interventions. The accuracy and efficacy of proposed models vary, indicating that AI has the potential to improve the level of personalization of mental health therapies. The results indicate that AI-driven techniques can significantly improve the effectiveness and efficiency of e-therapy platforms.