Development of a Classification Model for Mental Health
Authors
Wei Yihai
Share
Annotation
Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. This paper will use the logistic regression to classify whether the person sought to have some mental health treatment. The final model results show that our model has achieved an AUC value of over 0.86, indicating a powerful strength to predict the mental health reorganization of individuals. Also, the report presented that the top factors that influence the mental health condition are age, gender and family history.
Keywords
Authors
Wei Yihai
Share
References:
[1] Pedrelli, P., Nyer, M., Yeung, A., Zulauf, C., & Wilens, T. (2015). College students: mental health problems and treatment considerations. Academic Psychiatry, 39(5), 503-511.
[2] Hilbe, J. M. (2009). Logistic regression models. Chapman and hall/CRC.
[3] Wang, H., Xu, Q., & Zhou, L. (2015). Large unbalanced credit scoring using lasso-logistic regression ensemble. PloS one, 10(2), e0117844.
[4] Narkhede, S. (2018). Understanding auc-roc curve. Towards Data Science, 26, 220-227.
[5] Malhi, A., & Gao, R. X. (2004). PCA-based feature selection scheme for machine defect classification. IEEE transactions on instrumentation and measurement, 53(6), 1517-1525.
[6] Essex, M. J., Kraemer, H. C., Armstrong, J. M., Boyce, W. T., Goldsmith, H. H., Klein, M. H., ... & Kupfer, D. J. (2006). Exploring risk factors for the emergence of children's mental health problems. Archives of general psychiatry, 63(11), 1246-1256.
[7] Kirasich, K., Smith, T., & Sadler, B. (2018). Random forest vs logistic regression: binary classification for heterogeneous datasets. SMU Data Science Review, 1(3), 9.