Supervised machine learning in predicting depression, anxiety and stress using web-based big data: preserving the humanistic intellect
Introduction; The validated Depression, Anxiety and Stress Scale, 21 items (DASS-21) offers an insight on categorizing individuals into severity of each condition. The advancement in public health big data provides a platform for early detection and prompt treatment of individuals. However, ther...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/90102/1/ID%20166%20POSTER%203WCII.pdf http://irep.iium.edu.my/90102/7/Abstract%20Book%203WCII.pdf http://irep.iium.edu.my/90102/ |
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| Summary: | Introduction; The validated Depression, Anxiety and Stress Scale, 21 items (DASS-21) offers an insight on categorizing
individuals into severity of each condition. The
advancement in public health big data provides a platform
for early detection and prompt treatment of individuals.
However, there are lacking evidence on prediction accuracy
of these data using artificial intelligence methods.
Objectives
1. To determine accuracy of supervised machine learning
in predicting depression, anxiety and stress using big
data.
2. To determine the most important predictor of
depression, anxiety and stress using machine learning
model
Method; Cross sectional study using secondary data obtained from an online resource center was conducted, involving 339,781 respondents.
Outcomes were depression, anxiety and stress were
measured using DASS21. Each outcome was modelled with the rest of the outcome, plus gender, age, race, marital status, education level and occupational status. Feed-forward artificial neural network was modelled using multilayer perceptron machine learning procedure
using IBM SPSS version 2 |
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