Predicting mental disorder from noisy questionnaires: an anomaly detection approach based on keyword extraction and machine learning techniques

Article type: Research Article

Authors: Zhou, Qing^{; *} | Shi, Xi | Ge, Liang

Affiliations: College of Computer Science, Chongqing University, Chongqing, China

Correspondence: [*] Corresponding author. Qing Zhou, College of Computer Science, Chongqing University, Chongqing, China. E-mail: [email protected].

Abstract: The early warning of mental disorders is of great importance for the psychological well-being of college students. The accuracy of conventional scaling methods on questionnaires is generally low in predicting mental disorders, as the questionnaires contain much noise, and the processing on the questionnaires is rudimentary. To address this problem, we propose a novel anomaly detection framework on questionnaires, which represents each questionnaire as a document, and applies keyword extraction and machine learning techniques to detect abnormal questionnaires. We also propose a new keyword statistic for the calculation of option significance and three interpretable machine learning models for the calculation of question significance. Experiments demonstrate the effectiveness of our proposed methods.

Keywords: Mental health, text analysis, interpretability, TF-IDF, Likert scale

DOI: 10.3233/JIFS-211044

Journal: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 7167-7179, 2021