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Stack Ensemble Model to Detect the Stress in Humans by Considering the Sleeping Habits

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dc.contributor.author Kanagarathnam, Mauran
dc.contributor.author Premisha, P.
dc.contributor.author Prasanth, Senthan
dc.contributor.author Banujan, Kuhaneswaran
dc.contributor.author Kumara, B.T.G.S.
dc.date.accessioned 2023-02-17T04:05:10Z
dc.date.available 2023-02-17T04:05:10Z
dc.date.issued 2022
dc.identifier.citation Kanagarathnam Mauran.; Premisha P.; Prasanth Senthan; Banujan Kuhaneswaran; Kumara B.T.G.S. (2022), Stack Ensemble Model to Detect the Stress in Humans by Considering the Sleeping Habits, 7th International Conference on Advances in Technology and Computing (ICATC 2022), Faculty of Computing and Technology, University of Kelaniya Sri Lanka. Page 41 -45. en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/25977
dc.description.abstract Recently, one of the big challenges encountered by humans is experiencing and managing stress. Beyond the age restriction, people of all ages, from teenagers to seniors, experience issues as a result of stress. Acute and chronic stress are the two main categories of stress. Acute stress is a typical human response that aids in your body’s adaptation to a new situation. In actuality, this form of stress has positive effects. However, the second type of stress, chronic stress, is a crucial type of stress, and this study focused on determining the stress level of this type in advance. This research examined eight attributes related to chronic stress to investigate the chosen person’s sleeping patterns. The Kaggle website provided the dataset that was used in this study. The user’s snoring range, body temperature, limb movement rate, blood oxygen levels, eye movement, number of hours of sleep, heart rate, and stress levels (0-low/normal, 1-medium low, 2-medium, 3-medium high, 4 - high) were all taken into account. The stack ensemble approach was utilized with two levels during this approach. At level 0, the classifiers such as Random Forest, Decision tree, K-nearest neighbour, and XGBoost were considered. At level 1, as a Metamodel, Logistic regression was adopted. Moreover, the predictions obtained from the level 0 models added an additional attribute to the original dataset and fed it to the level 1 model as a new training dataset. Additionally, five folds of fold cross-validation were performed along with the basic assessment to validate further the model for various ratios of training and testing data. Following the cross-validation, the model’s mean accuracy obtained for RF, DT, KNN, XGB and stack ensemble models. From the results discovered, it was represented that the combined model (stack ensemble model) produced more precise results rather than the models considered in isolation. en_US
dc.publisher Faculty of Computing and Technology, University of Kelaniya Sri Lanka en_US
dc.subject Stack Ensemble Model, Chronic Stress, Meta Model, Machine Learning en_US
dc.title Stack Ensemble Model to Detect the Stress in Humans by Considering the Sleeping Habits en_US


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