Prediction of adolescent subjective well-being: A machine learning approach.

BACKGROUND: Subjective well-being (SWB), also known as happiness, plays an important role in evaluating both mental and physical health. Adolescents deserve specific attention because they are under a great variety of stresses and are at risk for mental disorders during adulthood. AIM: The present paper aims to predict undergraduate students' SWB by machine learning method. METHODS: Gradient Boosting Classifier which was an innovative yet validated machine learning approach was used to analyse data from 10 518 Chinese adolescents. The online survey included 298 factors such as depression and personality. Quality control procedure was used to minimise biases due to online survey reports. We applied feature selection to achieve the balance between optimal prediction and result interpretation. RESULTS: The top 20 happiness risks and protective factors were finally brought into the predicting model. Approximately 90% individuals' SWB can be predicted correctly, and the sensitivity and specificity were about 92% and 90%, respectively. CONCLUSIONS: This result identifies at-risk individuals according to new characteristics and established the foundation for adolescent prevention strategies.