Machine Learning and Stress Assessment: A Review

Stress assessment has been considered essentials in the early stages because stress-related abnormalities tend to increase the risk of strokes, heart attacks, depression, and hypertension. This may also induce suicidal thought within the victims of this neurological state. The CAD (Computer Aided Di...

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Main Authors: Faraz, S., Ali, S.S.A., Adil, S.H.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.23556 /
Published: Institute of Electrical and Electronics Engineers Inc. 2019
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063463913&doi=10.1109%2fICEEST.2018.8643313&partnerID=40&md5=13f210f328c47ecb580086e7d3a348da
http://eprints.utp.edu.my/23556/
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id utp-eprints.23556
recordtype eprints
spelling utp-eprints.235562021-08-19T07:56:54Z Machine Learning and Stress Assessment: A Review Faraz, S. Ali, S.S.A. Adil, S.H. Stress assessment has been considered essentials in the early stages because stress-related abnormalities tend to increase the risk of strokes, heart attacks, depression, and hypertension. This may also induce suicidal thought within the victims of this neurological state. The CAD (Computer Aided Diagnosis) have been a way forward for both medical experts and people with complications. The recent development of Machine learning revolution has proved to be substantial for medical diagnosis and prediction. This approach can further be used with neurological tools. The initial status of the brain activities would act as a window into the brain; which can be used as an insight. With the influence of machine learning more generalized way of discriminating stress activities with other normal activities can be possible. © 2018 IEEE. Institute of Electrical and Electronics Engineers Inc. 2019 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063463913&doi=10.1109%2fICEEST.2018.8643313&partnerID=40&md5=13f210f328c47ecb580086e7d3a348da Faraz, S. and Ali, S.S.A. and Adil, S.H. (2019) Machine Learning and Stress Assessment: A Review. In: UNSPECIFIED. http://eprints.utp.edu.my/23556/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Stress assessment has been considered essentials in the early stages because stress-related abnormalities tend to increase the risk of strokes, heart attacks, depression, and hypertension. This may also induce suicidal thought within the victims of this neurological state. The CAD (Computer Aided Diagnosis) have been a way forward for both medical experts and people with complications. The recent development of Machine learning revolution has proved to be substantial for medical diagnosis and prediction. This approach can further be used with neurological tools. The initial status of the brain activities would act as a window into the brain; which can be used as an insight. With the influence of machine learning more generalized way of discriminating stress activities with other normal activities can be possible. © 2018 IEEE.
format Conference or Workshop Item
author Faraz, S.
Ali, S.S.A.
Adil, S.H.
spellingShingle Faraz, S.
Ali, S.S.A.
Adil, S.H.
Machine Learning and Stress Assessment: A Review
author_sort Faraz, S.
title Machine Learning and Stress Assessment: A Review
title_short Machine Learning and Stress Assessment: A Review
title_full Machine Learning and Stress Assessment: A Review
title_fullStr Machine Learning and Stress Assessment: A Review
title_full_unstemmed Machine Learning and Stress Assessment: A Review
title_sort machine learning and stress assessment: a review
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2019
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063463913&doi=10.1109%2fICEEST.2018.8643313&partnerID=40&md5=13f210f328c47ecb580086e7d3a348da
http://eprints.utp.edu.my/23556/
_version_ 1741196694600548352
score 11.62408

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