Classification model for mental health of employees based on variable encoder techniques

Pinqi Jiao

doi:10.1117/12.2691821

23 August 2023 Classification model for mental health of employees based on variable encoder techniques

Pinqi Jiao

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 1278408 (2023) https://doi.org/10.1117/12.2691821
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

The detection of mental health problems as early as possible is very critical for social development as well as for healthcare industry development. In this paper, logistic regression with variable encoder techniques will be used to classify mental health problems of employees. The model has a promising ability to predict whether an employee has a mental health issue using its basic information including gender, age, work, and other related information. The model reaches a high accuracy and high AUC values in the testing procedure, meaning the model can be further used in other binary prediction areas.

Citation Download Citation

Pinqi Jiao "Classification model for mental health of employees based on variable encoder techniques", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 1278408 (23 August 2023); https://doi.org/10.1117/12.2691821

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