Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening
Abstract
:1. Introduction
2. MCI Prediction Algorithms
2.1. Data Collection
2.2. Data Preprocessing
2.2.1. EEG Preprocessing
2.2.2. ET Preprocessing
2.2.3. NTB Data Preprocessing
2.3. Feature Extraction
2.3.1. EEG Data
2.3.2. ET Data
2.3.3. NTB Data
2.4. Feature Selection
2.5. Classification
3. Materials and Methods
3.1. Subjects
3.2. Data Acquisition
3.3. Validation Experiments for Optimal Parameters of the Classifier
3.4. Discriminative Analysis
3.5. Statistical Analysis
4. Results
4.1. Demographic and Clinical Characteristics
4.2. Validation Experiments for Optimal Parameters of Classifier
4.3. Discriminative Analysis
5. Discussion
- (1)
- In terms of feature extraction, the linear and nonlinear feature analysis has been successfully used to identify the powerful biomarkers of neurophysiological diseases, such as Alzheimer’s disease (AD). In this study, we applied both linear and nonlinear methods to extract EEG and eye movement features. For EEG, complexity analysis as a nonlinear dynamic method can represent the rate of new patterns appearing in a time series, and to a certain extent, details of the signal can be presented in the binarized sequence.
- (2)
- In terms of feature selection and classification, the SVM model was selected. As a ML model, the SVM is suitable for classifying the features obtained from neuropsychological assessments.
- (3)
- In terms of the clinical setting, we depicted a machine learning framework for automated cognitive assessment data analysis for the precise classification of healthy and mild cognitive impairment individuals. Our work opens the possibility for automated assessment of cognitive function in community screening.
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Cohort 1 | Cohort 2 | |||||
---|---|---|---|---|---|---|
NC (184) | MCI (152) | p Value | NC (48) | MCI (44) | p Value | |
Age (years) | 71.7 ± 4.66 | 71.6 ± 4.15 | 0.875 b | 69.3 ± 17.0 | 76.5 ± 11.3 | 0.783 b |
Education (years) | 9.36 ± 3.47 | 8.16 ± 3.74 | 0.541 a | 13.0 ± 3.87 | 10.8 ± 5.66 | 0.492 a |
Gender (male/female) | 101/83 | 78/74 | 0.071 c | 18/30 | 16/28 | 0.068 c |
MoCA-B | 28.3 ± 0.95 | 23.2 ± 3.40 | <0.001 b * | 23.8 ± 3.28 | 16.4 ± 3.84 | <0.001 b * |
ACE-R | 72.1 ± 7.79 | 63.7 ± 8.53 | <0.001 b * | 71.0 ± 24.9 | 64.2 ± 8.28 | <0.001 b * |
Kernel Function | C | GAMMA | Accuracy (%) | Sensitivity (%) | Specificity (%) | AUC (95% CI) |
---|---|---|---|---|---|---|
Linear | 4.0 | / | 84.3 ± 4.05 | 83.1 ± 7.70 | 85.5 ± 4.97 | 0.906 (0.841–0.969) |
Poly | 20.0 | 0.02 | 78.1 ± 9.90 | 83.6 ± 10.6 | 71.3 ± 13.6 | 0.851 (0.747–0.954) |
RBF | 1.1 | 0.001 | 84.5 ± 4.34 | 82.4 ± 7.36 | 86.5 ± 6.51 | 0.934 (0.878–0.977) |
Sigmoid | 17.0 | 0.01 | 82.1 ± 6.08 | 90.9 ± 8.13 | 71.3 ± 11.7 | 0.851 (0.838–0.964) |
Comparative Model | Accuracy (%) | Sensitivity (%) | Specificity (%) | AUC (95% CI) |
---|---|---|---|---|
The clinical model | 62.6 ± 5.19 | 54.7 ± 6.81 | 71.4 ± 5.56 | 0.653 (0.541–0.783) |
Single neuropsychological test model | 75.6 ± 4.60 | 55.7 ± 8.15 | 71.2 ± 4.72 | 0.8014 (0.700–0.885) |
Single physiological test model | 81.4 ± 4.66 | 72.1 ± 8.25 | 89.2 ± 5.42 | 0.9045 (0.819–0.961) |
The proposed tool model | 84.5 ± 4.43 | 81.9± 7.88 | 86.8 ± 6.19 | 0.9415 (0.893–0.982) |
Comparative Model | Accuracy (%) | Sensitivity (%) | Specificity (%) | AUC (95% CI) |
---|---|---|---|---|
The clinical model | 65.7 ± 4.93 | 43.3 ± 10.6 | 90.1 ± 7.94 | 0.660 (0.543–0.789) |
Single neuropsychological test model | 75.0 ± 5.22 | 54.1 ± 8.63 | 91.5 ± 4.73 | 0.803 (0.681–0.889) |
Single physiological test model | 87.0 ± 4.27 | 82.4 ± 7.94 | 90.6 ± 5.05 | 0.937 (0.867–0.985) |
The proposed tool model | 88.8 ± 3.59 | 86.2 ± 6.46 | 91.0 ± 5.39 | 0.966 (0.921–0.988) |
Detection Tools | Modality | Subject | Method | Classifier | Accuracy |
---|---|---|---|---|---|
EEG based | Siuly, 2020 [33] EEG (19 Electrodes) | 27 | EEG features | ELM | 98.8% |
ET based | Lagun, 2011 [34] ET Test | 174 | ET features | SVM | 87% |
Neuropsychological test based | Yim, 2020 [15] | 614 | The mean total scores of neuropsychological test | GB | 93.5% |
NTB based | Wang, 2022 [35] Neuropsychological tests battery | 241 | NTB scores | RF | 68% |
Proposed Method NTB, EEG and Eye tracking | EEG (1 electrode) & ET & Neuropsychological test battery | 336 | EEG & ET features & NTB scores | SVM | 88.8% |
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Jiang, J.; Zhang, J.; Li, C.; Yu, Z.; Yan, Z.; Jiang, J. Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening. Brain Sci. 2022, 12, 1149. https://doi.org/10.3390/brainsci12091149
Jiang J, Zhang J, Li C, Yu Z, Yan Z, Jiang J. Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening. Brain Sciences. 2022; 12(9):1149. https://doi.org/10.3390/brainsci12091149
Chicago/Turabian StyleJiang, Juanjuan, Jieming Zhang, Chenyang Li, Zhihua Yu, Zhuangzhi Yan, and Jiehui Jiang. 2022. "Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening" Brain Sciences 12, no. 9: 1149. https://doi.org/10.3390/brainsci12091149