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 stre...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 students1 SWB by machine learning method.Methods Gradient Boosting Classifier which was an innovative yet validated machine learning approach was used to analyse data from 10518 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.展开更多
基金This work was supported by the National Key Research and Development Program(2016YFC0906400,2016YFC1307000,2016YFC0905000)the National Nature Science Foundation of China(81421061,81361120389)+2 种基金the Shanghai Key Laboratory of Psychotic Disorders(13dz2260500)the Shanghai Leading Academic Discipline Project(B205)the Fundamental Research Funds for the Central Universities(16JXRZ01).
文摘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 students1 SWB by machine learning method.Methods Gradient Boosting Classifier which was an innovative yet validated machine learning approach was used to analyse data from 10518 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.
