Dataset Information

Identification of Dementia & Mild Cognitive Impairment in Chinese Elderly Using Machine Learning.

ABSTRACT:

Objective

To assess the role of Machine Learning (ML) in identification critical factors of dementia and mild cognitive impairment.

Methods

371 elderly individuals were ultimately included in the ML analysis. Demographic information (including gender, age, parity, visual acuity, auditory function, mobility, and medication history) and 35 features from 10 assessment scales were used for modeling. Five machine learning classifiers were used for evaluation, employing a procedure involving feature extraction, selection, model training, and performance assessment to identify key indicative factors.

Results

The Random Forest model, after data preprocessing, Information Gain, and Meta-analysis, utilized three training features and four meta-features, achieving an area under the curve of 0.961 and a accuracy of 0.894, showcasing exceptional accuracy for the identification of dementia and mild cognitive impairment.

Conclusions

ML serves as a identification tool for dementia and mild cognitive impairment. Using Information Gain and Meta-feature analysis, Clinical Dementia Rating (CDR) and Neuropsychiatric Inventory (NPI) scale information emerged as crucial for training the Random Forest model.

SUBMITTER: Ying TT

PROVIDER: S-EPMC11320688 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature

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Publications

Identification of Dementia & Mild Cognitive Impairment in Chinese Elderly Using Machine Learning.

Ying Tong-Tong TT Zhuang Li-Ying LY Xu Shan-Hu SH Zhang Shu-Feng SF Huang Li-Jun LJ Gao Wei-Wei WW Liu Lu L Lai Qi-Lun QL Lou Yue Y Liu Xiao-Li XL

American journal of Alzheimer's disease and other dementias 20240101

<h4>Objective</h4>To assess the role of Machine Learning (ML) in identification critical factors of dementia and mild cognitive impairment.<h4>Methods</h4>371 elderly individuals were ultimately included in the ML analysis. Demographic information (including gender, age, parity, visual acuity, auditory function, mobility, and medication history) and 35 features from 10 assessment scales were used for modeling. Five machine learning classifiers were used for evaluation, employing a procedure invo ...[more]

PMID: 39133478

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Dataset Information

Identification of Dementia & Mild Cognitive Impairment in Chinese Elderly Using Machine Learning.

Objective

Methods

Results

Conclusions

Publications

Identification of Dementia & Mild Cognitive Impairment in Chinese Elderly Using Machine Learning.

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