{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T11:03:07Z","timestamp":1784545387660,"version":"3.55.0"},"reference-count":59,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research and Innovation, Ministry of Education in Saudi Arabia","award":["223202"],"award-info":[{"award-number":["223202"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.<\/jats:p>","DOI":"10.3390\/s23156664","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T01:09:01Z","timestamp":1690333741000},"page":"6664","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8665-1669","authenticated-orcid":false,"given":"Ahmad","family":"Almadhor","sequence":"first","affiliation":[{"name":"Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2354-4409","authenticated-orcid":false,"given":"Gabriel Avelino","family":"Sampedro","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Studies, University of the Philippines Open University, Los Ba\u00f1os 4031, Philippines"},{"name":"Center for Computational Imaging and Visual Innovations, De La Salle University, Manila 1004, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4215-7260","authenticated-orcid":false,"given":"Mideth","family":"Abisado","sequence":"additional","affiliation":[{"name":"College of Computing and Information Technologies, National University, Manila 1008, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8204-4896","authenticated-orcid":false,"given":"Sidra","family":"Abbas","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University, Islamabad 22060, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Almadhor, A., Sampedro, G.A., Abisado, M., Abbas, S., Kim, Y.J., Khan, M.A., Baili, J., and Cha, J.H. 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