Universiti Teknologi Malaysia Institutional Repository

Depression anxiety stress scale and handgrip using machine learning analysis

Usman, Sahnius and Rusli, Fatin ‘Aliah and A. Jalil, Siti Zura and Bani, Nurul Aini (2022) Depression anxiety stress scale and handgrip using machine learning analysis. In: 4th International Conference on Smart Sensors and Application, ICSSA 2022, 26 - 28 July 2022, Kuala Lumpur, Malaysia.

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Official URL: http://dx.doi.org/10.1109/ICSSA54161.2022.9870948


Stress is an emotional or physical state of tension. Stress is the body's natural response to difficulty or a great deal of work. Each of us has a unique reaction to stress. Our capacity for adaptation can be influenced by our genetics, early life events, personality, and socioeconomic situations. This study used handgrip strength (HGS) reading for stress level screening together with Depression Anxiety Stress Scale (DASS) as an early assessment tool. This data of DASS and HGS were analyzed using Random Forest and Support Vector Machine. The dataset is normalized between 0 to 1 due to different units in different measurement tools. The result shows that Random Forest gives an accuracy of 93.75%, a specificity of 94.90%, and a sensitivity of 93.80%. However, SVM gives 87.50% accuracy, 90.30% specificity, and 87.50% sensitivity. This concludes that the Random Forest is better than SVM in terms of stress level classification.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:DASS, handgrip, Random Forest, SVM
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:98915
Deposited By: Widya Wahid
Deposited On:08 Feb 2023 13:22
Last Modified:08 Feb 2023 13:22

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