Machine Learning Techniques for Classification of Stress Levels in Traffic
The aim of this study is to apply Machine Learning techniques for predicting and classifying the stress level of people commuting from home to work and also to evaluate the performance of prediction models using feature selection. The database was obtained through a structured questionnaire with 44...
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| Format: | Article |
| Language: | English |
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Universidade Federal de Pernambuco (UFPE)
2024-06-01
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| Series: | Socioeconomic Analytics |
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| Online Access: | https://periodicos.ufpe.br/revistas/index.php/SECAN/article/view/262686 |
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| _version_ | 1825201951137071104 |
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| author | Amanda Trojan Fenerich Egídio José Romanelli Rodrigo Eduardo Catai Maria Teresinha Arns Steiner |
| author_facet | Amanda Trojan Fenerich Egídio José Romanelli Rodrigo Eduardo Catai Maria Teresinha Arns Steiner |
| author_sort | Amanda Trojan Fenerich |
| collection | DOAJ |
| description |
The aim of this study is to apply Machine Learning techniques for predicting and classifying the stress level of people commuting from home to work and also to evaluate the performance of prediction models using feature selection. The database was obtained through a structured questionnaire with 44 questions, applied to 196 people in the city of Curitiba, PR. The classification algorithms used were Support Vector Machine (SVM), Bayesian Networks (BN), and Logistic Regression (LR), comparatively. The results indicate that the classification of stress levels of new instances (people) as “high” or “low” can be performed using the LR technique (presenting the highest accuracy, 83.67%).
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| format | Article |
| id | doaj-art-d7a2c3d01a8a491b988daa119eb64f23 |
| institution | Kabale University |
| issn | 2965-4661 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Universidade Federal de Pernambuco (UFPE) |
| record_format | Article |
| series | Socioeconomic Analytics |
| spelling | doaj-art-d7a2c3d01a8a491b988daa119eb64f232025-02-07T17:46:10ZengUniversidade Federal de Pernambuco (UFPE)Socioeconomic Analytics2965-46612024-06-012110.51359/2965-4661.2024.262686Machine Learning Techniques for Classification of Stress Levels in TrafficAmanda Trojan Fenerich0Egídio José Romanelli1Rodrigo Eduardo Catai2Maria Teresinha Arns Steiner3University of GalwayFederal University of ParanáFederal University of Technology ParanáPontifical Catholic University of Paraná The aim of this study is to apply Machine Learning techniques for predicting and classifying the stress level of people commuting from home to work and also to evaluate the performance of prediction models using feature selection. The database was obtained through a structured questionnaire with 44 questions, applied to 196 people in the city of Curitiba, PR. The classification algorithms used were Support Vector Machine (SVM), Bayesian Networks (BN), and Logistic Regression (LR), comparatively. The results indicate that the classification of stress levels of new instances (people) as “high” or “low” can be performed using the LR technique (presenting the highest accuracy, 83.67%). https://periodicos.ufpe.br/revistas/index.php/SECAN/article/view/262686Artificial IntelligenceMachine Learningphysiological stresstraffictraffic studiesSupport Vector Machine |
| spellingShingle | Amanda Trojan Fenerich Egídio José Romanelli Rodrigo Eduardo Catai Maria Teresinha Arns Steiner Machine Learning Techniques for Classification of Stress Levels in Traffic Socioeconomic Analytics Artificial Intelligence Machine Learning physiological stress traffic traffic studies Support Vector Machine |
| title | Machine Learning Techniques for Classification of Stress Levels in Traffic |
| title_full | Machine Learning Techniques for Classification of Stress Levels in Traffic |
| title_fullStr | Machine Learning Techniques for Classification of Stress Levels in Traffic |
| title_full_unstemmed | Machine Learning Techniques for Classification of Stress Levels in Traffic |
| title_short | Machine Learning Techniques for Classification of Stress Levels in Traffic |
| title_sort | machine learning techniques for classification of stress levels in traffic |
| topic | Artificial Intelligence Machine Learning physiological stress traffic traffic studies Support Vector Machine |
| url | https://periodicos.ufpe.br/revistas/index.php/SECAN/article/view/262686 |
| work_keys_str_mv | AT amandatrojanfenerich machinelearningtechniquesforclassificationofstresslevelsintraffic AT egidiojoseromanelli machinelearningtechniquesforclassificationofstresslevelsintraffic AT rodrigoeduardocatai machinelearningtechniquesforclassificationofstresslevelsintraffic AT mariateresinhaarnssteiner machinelearningtechniquesforclassificationofstresslevelsintraffic |