Anxiety in young people: Analysis from a machine learning model

Abstract

The study addresses the detection of anxiety symptoms in young people using artificial intelligence models. Questionnaires such as the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder 7-item scale (GAD-7) are used to collect data, with a focus on early detection of anxiety. Three machine learning models are employed: Support Vector Machine (SVM), K Nearest Neighbors (KNN), and Random Forest (RF), with cross-validation to assess their effectiveness. Results show that the RF model is the most efficient, with an accuracy of 91 %, surpassing previous studies. Significant predictors of anxiety are identified, such as parental education level, alcohol consumption, and social security affiliation. A relationship is observed between anxiety and personal and family history of mental illness, as well as with characteristics external to the model, such as family and personal history of depression. The analysis of the results highlights the importance of considering not only clinical but also social and family aspects in mental health interventions. It is suggested that the sample size be expanded in future studies to improve the robustness of the model. In summary, the study demonstrates the usefulness of artificial intelligence in the early detection of anxiety in young people and highlights the relevance of addressing multidimensional factors in the assessment and treatment of this condition.