Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study
Objectives Alzheimer’s disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Although existing AD risk prediction tools demonstrate high accuracy, their complexity and limited accessibility restrict practical application. This study...
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| Format: | Article |
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BMJ Publishing Group
2025-02-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/15/2/e092293.full |
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| author | Yanfei Chen Bing Wang Yankai Shi Wenhao Qi Shihua Cao Bingsheng Wang Ruihan Xie Jiani Yao Xiajing Lou Chaoqun Dong Xiaohong Zhu Danni He |
| author_facet | Yanfei Chen Bing Wang Yankai Shi Wenhao Qi Shihua Cao Bingsheng Wang Ruihan Xie Jiani Yao Xiajing Lou Chaoqun Dong Xiaohong Zhu Danni He |
| author_sort | Yanfei Chen |
| collection | DOAJ |
| description | Objectives Alzheimer’s disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Although existing AD risk prediction tools demonstrate high accuracy, their complexity and limited accessibility restrict practical application. This study aimed to develop a convenience, efficient prediction model for AD risk using machine learning techniques.Design and setting We conducted a cross-sectional study with participants aged 60 and older from the National Alzheimer’s Coordinating Center. We selected personal characteristics, clinical data and psychosocial factors as baseline predictors for AD (March 2015 to December 2021). The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. An oversampling method was applied to balance the data set.Interventions This study has no interventions.Participants The study included 2379 participants, of whom 507 were diagnosed with AD.Primary and secondary outcome measures Including accuracy, precision, recall, F1 score, etc.Results 11 variables were critical in the training phase, including educational level, depression, insomnia, age, Body Mass Index (BMI), medication count, gender, stenting, systolic blood pressure (sbp), neurosis and rapid eye movement. The XGBoost model exhibited superior performance compared with other models, achieving area under the curve of 0.915, sensitivity of 76.2% and specificity of 92.9%. The most influential predictors were educational level, total medication count, age, sbp and BMI.Conclusions The proposed classifier can help guide preclinical screening of AD in the elderly population. |
| format | Article |
| id | doaj-art-7edb6870273c4584afd8465f5243af50 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-7edb6870273c4584afd8465f5243af502025-02-09T04:35:15ZengBMJ Publishing GroupBMJ Open2044-60552025-02-0115210.1136/bmjopen-2024-092293Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional studyYanfei Chen0Bing Wang1Yankai Shi2Wenhao Qi3Shihua Cao4Bingsheng Wang5Ruihan Xie6Jiani Yao7Xiajing Lou8Chaoqun Dong9Xiaohong Zhu10Danni He11Hangzhou Normal University Affiliated Hospital, Hangzhou, Zhejiang, ChinaSchool of Nursing, Hangzhou Normal University, Hangzhou, Zhejiang, ChinaSchool of Nursing, Hangzhou Normal University, Hangzhou, Zhejiang, ChinaSchool of Nursing, Hangzhou Normal University, Hangzhou, Zhejiang, ChinaSchool of Nursing, Hangzhou Normal University, Hangzhou, Zhejiang, ChinaSchool of Nursing, Hangzhou Normal University, Hangzhou, Zhejiang, ChinaDepartment of Information Engineering, The Chinese University of Hong Kong, Hong Kong, ChinaSchool of Nursing, Hangzhou Normal University, Hangzhou, Zhejiang, ChinaSchool of Nursing, Hangzhou Normal University, Hangzhou, Zhejiang, ChinaSchool of Nursing, Hangzhou Normal University, Hangzhou, Zhejiang, ChinaSchool of Nursing, Hangzhou Normal University, Hangzhou, Zhejiang, ChinaZhejiang Provincial People`s Hospital, Hangzhou, Zhejiang, ChinaObjectives Alzheimer’s disease (AD) poses a significant challenge for individuals aged 65 and older, being the most prevalent form of dementia. Although existing AD risk prediction tools demonstrate high accuracy, their complexity and limited accessibility restrict practical application. This study aimed to develop a convenience, efficient prediction model for AD risk using machine learning techniques.Design and setting We conducted a cross-sectional study with participants aged 60 and older from the National Alzheimer’s Coordinating Center. We selected personal characteristics, clinical data and psychosocial factors as baseline predictors for AD (March 2015 to December 2021). The study utilised Random Forest and Extreme Gradient Boosting (XGBoost) algorithms alongside traditional logistic regression for modelling. An oversampling method was applied to balance the data set.Interventions This study has no interventions.Participants The study included 2379 participants, of whom 507 were diagnosed with AD.Primary and secondary outcome measures Including accuracy, precision, recall, F1 score, etc.Results 11 variables were critical in the training phase, including educational level, depression, insomnia, age, Body Mass Index (BMI), medication count, gender, stenting, systolic blood pressure (sbp), neurosis and rapid eye movement. The XGBoost model exhibited superior performance compared with other models, achieving area under the curve of 0.915, sensitivity of 76.2% and specificity of 92.9%. The most influential predictors were educational level, total medication count, age, sbp and BMI.Conclusions The proposed classifier can help guide preclinical screening of AD in the elderly population.https://bmjopen.bmj.com/content/15/2/e092293.full |
| spellingShingle | Yanfei Chen Bing Wang Yankai Shi Wenhao Qi Shihua Cao Bingsheng Wang Ruihan Xie Jiani Yao Xiajing Lou Chaoqun Dong Xiaohong Zhu Danni He Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study BMJ Open |
| title | Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study |
| title_full | Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study |
| title_fullStr | Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study |
| title_full_unstemmed | Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study |
| title_short | Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study |
| title_sort | advancing alzheimer s disease risk prediction development and validation of a machine learning based preclinical screening model in a cross sectional study |
| url | https://bmjopen.bmj.com/content/15/2/e092293.full |
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