The Prediction of Mental Stress by Utilizing Supervised and Unsupervised Machine Learning Techniques
Year: 2025
Type: article
Abstract: Mental Stress is the most prominent and pertinent issue nowadays in people all across the globe and can be considered a disease that needs to be addressed on time to avoid suicides. Artificial Intelligence plays a major role in resolving this societal issue by predicting the stress level. In this paper, a hybrid model using various Machine Learning algorithms is constructed to predict the stress based on some crucial parameters. The constructed hybrid model is hyper-tuned with Adaboost and Bagging techniques and the model's accuracy was raised to 96.4%. The stacked model accurately predicts the stress level and can provide concrete insights into the reasons that are highly responsible for stress among people of all ages.
Author: Amanpreet Kaur
Institution: CT University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410322762