Authors :
Shikha Verma; Aman Goyal; Santushti Gandhi
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/2fb2efxu
DOI :
https://doi.org/10.5281/zenodo.8330745
Abstract :
Objective: The study aimed to assess the
reliability and validity of the Psychological factors,
namely Depression, Anxiety, and Stress Scale-21 (DASS-
21), among University students in Delhi.
Methods: The DASS-21 questionnaire was administered
by conducting a survey where around 100 samples were
randomly selected. A comparison and training model
was formed using the benchmark dataset along with the
original data collected in the study. Three supervised
machine learning models were trained on the same. The
best model was selected and tested on the originally
collected data.Conclusion: The factors selected using machine learning
techniques affect an individual's severity. To further
verify these factors, practitioners were engaged to
identify the specific features that influence these
psychological parameters. These results helped to
understand the importance and use of machine learning
techniques for analyzing the severity of stress, anxiety
and depression scales amongst individuals. The testing
accuracy achieved was similar to the training accuracy
indicating the model did not have any anomaly and
could be used for predicting the severity of stress,
anxiety and depression among university students in
India.
Keywords :
Stress, Anxiety, Depression, Correlation, Supervised Machine Learning, Testing Accuracy.
Objective: The study aimed to assess the
reliability and validity of the Psychological factors,
namely Depression, Anxiety, and Stress Scale-21 (DASS-
21), among University students in Delhi.
Methods: The DASS-21 questionnaire was administered
by conducting a survey where around 100 samples were
randomly selected. A comparison and training model
was formed using the benchmark dataset along with the
original data collected in the study. Three supervised
machine learning models were trained on the same. The
best model was selected and tested on the originally
collected data.Conclusion: The factors selected using machine learning
techniques affect an individual's severity. To further
verify these factors, practitioners were engaged to
identify the specific features that influence these
psychological parameters. These results helped to
understand the importance and use of machine learning
techniques for analyzing the severity of stress, anxiety
and depression scales amongst individuals. The testing
accuracy achieved was similar to the training accuracy
indicating the model did not have any anomaly and
could be used for predicting the severity of stress,
anxiety and depression among university students in
India.
Keywords :
Stress, Anxiety, Depression, Correlation, Supervised Machine Learning, Testing Accuracy.
