Classification of Cognitive Frailty in Elderly People from Blood Samples using Machine Learning

Cognitive Frailty (CF) is a prevalent age-related disease that is affecting many individuals worldwide. Medical intervention needs to be timely, as the late stages of CF prove to be challenging for both clinicians and caretakers. While the existing clinical diagnosis and screening tools for CF are c...

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Main Authors: Idris, S., Badruddin, N.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29127 /
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125458452&doi=10.1109%2fBHI50953.2021.9508514&partnerID=40&md5=4757d25b715d8ad6d657a93014c59867
http://eprints.utp.edu.my/29127/
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id utp-eprints.29127
recordtype eprints
spelling utp-eprints.291272022-03-25T00:57:55Z Classification of Cognitive Frailty in Elderly People from Blood Samples using Machine Learning Idris, S. Badruddin, N. Cognitive Frailty (CF) is a prevalent age-related disease that is affecting many individuals worldwide. Medical intervention needs to be timely, as the late stages of CF prove to be challenging for both clinicians and caretakers. While the existing clinical diagnosis and screening tools for CF are capable of detecting the syndrome, a means of prediction is needed in order to identify CF in older adults before its onset. This paper proposes a machine learning model to classify patients into different levels of CF, using parameters from blood samples. A total of 7 different classification algorithms were used to predict between 6 levels of CF, the Robust and Non-Robust groups, as well as the Robust and Frail with MCI groups. The binary classification for Robust and Frail with MCI achieved the highest accuracy, with Gaussian Naïve Bayes showing the highest holdout method accuracy of 70.5, as well as the highest cross validation accuracy of 74. © 2021 IEEE Institute of Electrical and Electronics Engineers Inc. 2021 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125458452&doi=10.1109%2fBHI50953.2021.9508514&partnerID=40&md5=4757d25b715d8ad6d657a93014c59867 Idris, S. and Badruddin, N. (2021) Classification of Cognitive Frailty in Elderly People from Blood Samples using Machine Learning. In: UNSPECIFIED. http://eprints.utp.edu.my/29127/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Cognitive Frailty (CF) is a prevalent age-related disease that is affecting many individuals worldwide. Medical intervention needs to be timely, as the late stages of CF prove to be challenging for both clinicians and caretakers. While the existing clinical diagnosis and screening tools for CF are capable of detecting the syndrome, a means of prediction is needed in order to identify CF in older adults before its onset. This paper proposes a machine learning model to classify patients into different levels of CF, using parameters from blood samples. A total of 7 different classification algorithms were used to predict between 6 levels of CF, the Robust and Non-Robust groups, as well as the Robust and Frail with MCI groups. The binary classification for Robust and Frail with MCI achieved the highest accuracy, with Gaussian Naïve Bayes showing the highest holdout method accuracy of 70.5, as well as the highest cross validation accuracy of 74. © 2021 IEEE
format Conference or Workshop Item
author Idris, S.
Badruddin, N.
spellingShingle Idris, S.
Badruddin, N.
Classification of Cognitive Frailty in Elderly People from Blood Samples using Machine Learning
author_sort Idris, S.
title Classification of Cognitive Frailty in Elderly People from Blood Samples using Machine Learning
title_short Classification of Cognitive Frailty in Elderly People from Blood Samples using Machine Learning
title_full Classification of Cognitive Frailty in Elderly People from Blood Samples using Machine Learning
title_fullStr Classification of Cognitive Frailty in Elderly People from Blood Samples using Machine Learning
title_full_unstemmed Classification of Cognitive Frailty in Elderly People from Blood Samples using Machine Learning
title_sort classification of cognitive frailty in elderly people from blood samples using machine learning
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125458452&doi=10.1109%2fBHI50953.2021.9508514&partnerID=40&md5=4757d25b715d8ad6d657a93014c59867
http://eprints.utp.edu.my/29127/
_version_ 1741197192897495040
score 11.62408

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