Differentiating Alzheimer’s disease from mild cognitive impairment: a quick screening tool based on machine learning
Background Alzheimer’s disease (AD) is a neurodegenerative disorder characterised by cognitive decline, behavioural and psychological symptoms of dementia (BPSD) and impairment of activities of daily living (ADL). Early differentiation of AD from mild cognitive impairment (MCI) is necessary.Methods...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2023-12-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/13/12/e073011.full |
| _version_ | 1797370585491177472 |
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| author | Meiwei Zhang Yang Lu Weihong Kuang Lihua Chen Wenbo Zhang Wenqi Lu Juan Yu Weihua Yu |
| author_facet | Meiwei Zhang Yang Lu Weihong Kuang Lihua Chen Wenbo Zhang Wenqi Lu Juan Yu Weihua Yu |
| author_sort | Meiwei Zhang |
| collection | DOAJ |
| description | Background Alzheimer’s disease (AD) is a neurodegenerative disorder characterised by cognitive decline, behavioural and psychological symptoms of dementia (BPSD) and impairment of activities of daily living (ADL). Early differentiation of AD from mild cognitive impairment (MCI) is necessary.Methods A total of 458 patients newly diagnosed with AD and MCI were included. Eleven batteries were used to evaluate ADL, BPSD and cognitive function (ABC). Machine learning approaches including XGboost, classification and regression tree, Bayes, support vector machines and logical regression were used to build and verify the new tool.Results The Alzheimer’s Disease Assessment Scale (ADAS-cog) word recognition task showed the best importance in judging AD and MCI, followed by correct numbers of auditory verbal learning test delay recall and ADAS-cog orientation. We also provided a selected ABC-Scale that covered ADL, BPSD and cognitive function with an estimated completion time of 18 min. The sensitivity was improved in the four models.Conclusion The quick screen ABC-Scale covers three dimensions of ADL, BPSD and cognitive function with good efficiency in differentiating AD from MCI. |
| first_indexed | 2024-03-08T18:07:46Z |
| format | Article |
| id | doaj.art-b00cd43d13fd4b799e20355813f30d4e |
| institution | Directory Open Access Journal |
| issn | 2044-6055 |
| language | English |
| last_indexed | 2024-03-08T18:07:46Z |
| publishDate | 2023-12-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj.art-b00cd43d13fd4b799e20355813f30d4e2024-01-01T17:15:08ZengBMJ Publishing GroupBMJ Open2044-60552023-12-01131210.1136/bmjopen-2023-073011Differentiating Alzheimer’s disease from mild cognitive impairment: a quick screening tool based on machine learningMeiwei Zhang0Yang Lu1Weihong Kuang2Lihua Chen3Wenbo Zhang4Wenqi Lu5Juan Yu6Weihua Yu73 College of Electrical Engineering, Chongqing University, Chongqing, China1 Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China2 Department of Psychiatry, West China Hospital, Sichuan University, Chengdu, China4 Institutes of Neuroscience, Chongqing Medical University, Chongqing, China1 Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China1 Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China3 College of Electrical Engineering, Chongqing University, Chongqing, China4 Institutes of Neuroscience, Chongqing Medical University, Chongqing, ChinaBackground Alzheimer’s disease (AD) is a neurodegenerative disorder characterised by cognitive decline, behavioural and psychological symptoms of dementia (BPSD) and impairment of activities of daily living (ADL). Early differentiation of AD from mild cognitive impairment (MCI) is necessary.Methods A total of 458 patients newly diagnosed with AD and MCI were included. Eleven batteries were used to evaluate ADL, BPSD and cognitive function (ABC). Machine learning approaches including XGboost, classification and regression tree, Bayes, support vector machines and logical regression were used to build and verify the new tool.Results The Alzheimer’s Disease Assessment Scale (ADAS-cog) word recognition task showed the best importance in judging AD and MCI, followed by correct numbers of auditory verbal learning test delay recall and ADAS-cog orientation. We also provided a selected ABC-Scale that covered ADL, BPSD and cognitive function with an estimated completion time of 18 min. The sensitivity was improved in the four models.Conclusion The quick screen ABC-Scale covers three dimensions of ADL, BPSD and cognitive function with good efficiency in differentiating AD from MCI.https://bmjopen.bmj.com/content/13/12/e073011.full |
| spellingShingle | Meiwei Zhang Yang Lu Weihong Kuang Lihua Chen Wenbo Zhang Wenqi Lu Juan Yu Weihua Yu Differentiating Alzheimer’s disease from mild cognitive impairment: a quick screening tool based on machine learning BMJ Open |
| title | Differentiating Alzheimer’s disease from mild cognitive impairment: a quick screening tool based on machine learning |
| title_full | Differentiating Alzheimer’s disease from mild cognitive impairment: a quick screening tool based on machine learning |
| title_fullStr | Differentiating Alzheimer’s disease from mild cognitive impairment: a quick screening tool based on machine learning |
| title_full_unstemmed | Differentiating Alzheimer’s disease from mild cognitive impairment: a quick screening tool based on machine learning |
| title_short | Differentiating Alzheimer’s disease from mild cognitive impairment: a quick screening tool based on machine learning |
| title_sort | differentiating alzheimer s disease from mild cognitive impairment a quick screening tool based on machine learning |
| url | https://bmjopen.bmj.com/content/13/12/e073011.full |
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