Detection of cognitive impairment using a machine-learning algorithm

Young Chul Youn,1 Seong Hye Choi,2 Hae-Won Shin,1 Ko Woon Kim,3 Jae-Won Jang,4 Jason J Jung,5 Ging-Yuek Robin Hsiung,6 SangYun Kim7 1Department of Neurology, College of Medicine, Chung-Ang University, Seoul, South Korea; 2Department of Neurology, Inha University College of Medicine, Incheon, South...

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Autores principales:	Youn YC, Choi SH, Shin HW, Kim KW, Jang JW, Jung JJ, Hsiung GY, Kim SY
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2018
Materias:	Dementia Cognitive impairment Machine learning TensorFlow Mini-mental state examination Dementia questionnaire Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/2b1e8280a487417a8930bfdf54a0b890
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spelling	oai:doaj.org-article:2b1e8280a487417a8930bfdf54a0b8902021-12-02T00:37:55ZDetection of cognitive impairment using a machine-learning algorithm1178-2021https://doaj.org/article/2b1e8280a487417a8930bfdf54a0b8902018-11-01T00:00:00Zhttps://www.dovepress.com/detection-of-cognitive-impairment-using-a-machine-learning-algorithm-peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Young Chul Youn,1 Seong Hye Choi,2 Hae-Won Shin,1 Ko Woon Kim,3 Jae-Won Jang,4 Jason J Jung,5 Ging-Yuek Robin Hsiung,6 SangYun Kim7 1Department of Neurology, College of Medicine, Chung-Ang University, Seoul, South Korea; 2Department of Neurology, Inha University College of Medicine, Incheon, South Korea; 3Department of Neurology, Chonbuk National University Medical School and Hospital, Chonbuk, South Korea; 4Department of Neurology, Kangwon National University Hospital, Chuncheon, South Korea; 5Department of Computer Engineering, Chung-Ang University, Seoul, South Korea; 6Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada; 7Department of Neurology, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seoul, South Korea Purpose: The Mini-Mental State Examination (MMSE) is one of the most frequently used bedside screening measures of cognition. However, the Korean Dementia Screening Questionnaire (KDSQ) is an easier and more reliable screening method. Instead, other clinical variables and raw data were used for this study without the consideration of a cutoff value. The objective of this study was to develop a machine-learning algorithm for the detection of cognitive impairment (CI) based on the KDSQ and the MMSE.Patients and methods: The original dataset from the Clinical Research Center for Dementia of South Korea study was obtained. In total, 9,885 and 300 patients were randomly allocated to the training and test datasets, respectively. We selected up to 24 variables including sex, age, education duration, diabetes mellitus, and hypertension. We trained a machine-learning algorithm using TensorFlow based on the training dataset and then calculated its accuracy using the test dataset. The cost was calculated by conducting a logistic regression.Results: The accuracy of the model in predicting CI based on the KDSQ only, the MMSE only, and the combination of the KDSQ and MMSE was 84.3%, 88.3%, and 86.3%, respectively. For the KDSQ, the sensitivity for detecting CI was 91.50% and the specificity for detecting normal cognition (NL) was 59.60%. The sensitivity of the MMSE was 94.35%, and the specificity was 59.62%. When combining the KDSQ and the MMSE, the sensitivity for detecting CI was 91.5% and the specificity for detecting NL was 61.5%.Conclusion: The algorithm predicting CI based on the MMSE is superior. However, the KDSQ can be administered more easily in clinical practice and the algorithm using KDSQ is a comparable screening tool. Keywords: dementia, mild cognitive impairment, machine learning, TensorFlow, Mini-Mental State Examination, dementia questionnaireYoun YCChoi SHShin HWKim KWJang JWJung JJHsiung GYKim SYDove Medical PressarticleDementiaCognitive impairmentMachine learningTensorFlowMini-mental state examinationDementia questionnaireNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 14, Pp 2939-2945 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Dementia Cognitive impairment Machine learning TensorFlow Mini-mental state examination Dementia questionnaire Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	Dementia Cognitive impairment Machine learning TensorFlow Mini-mental state examination Dementia questionnaire Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Youn YC Choi SH Shin HW Kim KW Jang JW Jung JJ Hsiung GY Kim SY Detection of cognitive impairment using a machine-learning algorithm
description	Young Chul Youn,1 Seong Hye Choi,2 Hae-Won Shin,1 Ko Woon Kim,3 Jae-Won Jang,4 Jason J Jung,5 Ging-Yuek Robin Hsiung,6 SangYun Kim7 1Department of Neurology, College of Medicine, Chung-Ang University, Seoul, South Korea; 2Department of Neurology, Inha University College of Medicine, Incheon, South Korea; 3Department of Neurology, Chonbuk National University Medical School and Hospital, Chonbuk, South Korea; 4Department of Neurology, Kangwon National University Hospital, Chuncheon, South Korea; 5Department of Computer Engineering, Chung-Ang University, Seoul, South Korea; 6Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada; 7Department of Neurology, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seoul, South Korea Purpose: The Mini-Mental State Examination (MMSE) is one of the most frequently used bedside screening measures of cognition. However, the Korean Dementia Screening Questionnaire (KDSQ) is an easier and more reliable screening method. Instead, other clinical variables and raw data were used for this study without the consideration of a cutoff value. The objective of this study was to develop a machine-learning algorithm for the detection of cognitive impairment (CI) based on the KDSQ and the MMSE.Patients and methods: The original dataset from the Clinical Research Center for Dementia of South Korea study was obtained. In total, 9,885 and 300 patients were randomly allocated to the training and test datasets, respectively. We selected up to 24 variables including sex, age, education duration, diabetes mellitus, and hypertension. We trained a machine-learning algorithm using TensorFlow based on the training dataset and then calculated its accuracy using the test dataset. The cost was calculated by conducting a logistic regression.Results: The accuracy of the model in predicting CI based on the KDSQ only, the MMSE only, and the combination of the KDSQ and MMSE was 84.3%, 88.3%, and 86.3%, respectively. For the KDSQ, the sensitivity for detecting CI was 91.50% and the specificity for detecting normal cognition (NL) was 59.60%. The sensitivity of the MMSE was 94.35%, and the specificity was 59.62%. When combining the KDSQ and the MMSE, the sensitivity for detecting CI was 91.5% and the specificity for detecting NL was 61.5%.Conclusion: The algorithm predicting CI based on the MMSE is superior. However, the KDSQ can be administered more easily in clinical practice and the algorithm using KDSQ is a comparable screening tool. Keywords: dementia, mild cognitive impairment, machine learning, TensorFlow, Mini-Mental State Examination, dementia questionnaire
format	article
author	Youn YC Choi SH Shin HW Kim KW Jang JW Jung JJ Hsiung GY Kim SY
author_facet	Youn YC Choi SH Shin HW Kim KW Jang JW Jung JJ Hsiung GY Kim SY
author_sort	Youn YC
title	Detection of cognitive impairment using a machine-learning algorithm
title_short	Detection of cognitive impairment using a machine-learning algorithm
title_full	Detection of cognitive impairment using a machine-learning algorithm
title_fullStr	Detection of cognitive impairment using a machine-learning algorithm
title_full_unstemmed	Detection of cognitive impairment using a machine-learning algorithm
title_sort	detection of cognitive impairment using a machine-learning algorithm
publisher	Dove Medical Press
publishDate	2018
url	https://doaj.org/article/2b1e8280a487417a8930bfdf54a0b890
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Detection of cognitive impairment using a machine-learning algorithm

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