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Updated: Dec 11, 2025
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
An SBM-based machine learning model for identifying mild cognitive impairment in patients with Parkinson's disease
Jiahui Zhang1, You Li1, Yuyuan Gao1
1Department of Neurology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangdong Neuroscience Institute, No. 106 Zhongshan Er Road, Guangzhou 510080, China.
Objective:
To identify Parkinson's disease with mild cognitive impairment (PD-MCI) through surface-based morphometry (SBM) based machine learning model.
Methods:
93 patients with parkinson's disease (35 PD with normal cognition, 58 PD-MCI) were examined, obtaining 276 SBM variables per subject. 20 healthy control subjects were used as the reference. After extracting features with statistically significance, support vector machine (SVM) model with grid search method was applied to identify patients with PD-MCI. Accuracy, matthews correlation coefficient (MCC), receiver operating characteristic curve (ROC), precision-recall curve (PR), AUC-ROC, AUC-PR and leave-one-out cross validation (LOOCV) strategy were employed for model evaluation.
Results:
PD-MCI is characterized by widespread structural abnormality. SVM model with SBM features achieved an accuracy of 80.00% and area under the ROC of 0.86 for identifying PD-MCI. MCC, AUC-PR, and LOOCV classification accuracy were 0.56, 0.89, and 78.08%, respectively.
Conclusion:
Automatic identification of PD-MCI could be realized by SBM-based machine learning model.
Insights
This study developed a machine learning model using surface-based morphometry (SBM) to identify Parkinson's disease with mild cognitive impairment (PD-MCI). The model achieved high accuracy, demonstrating potential for early PD-MCI detection.
Area of Science:
- Neuroimaging
- Machine Learning
- Neurology
Background:
- Parkinson's disease (PD) can progress to mild cognitive impairment (PD-MCI).
- Early identification of PD-MCI is crucial for timely intervention.
- Surface-based morphometry (SBM) offers a way to analyze brain structure.
Purpose of the Study:
- To develop and evaluate a machine learning model for identifying PD-MCI.
- To utilize SBM features for classification of PD-MCI patients.
Main Methods:
- A support vector machine (SVM) model was trained using 276 SBM variables from 93 PD patients (35 with normal cognition, 58 with PD-MCI) and 20 healthy controls.
- Feature selection was performed on statistically significant variables.
- Model performance was assessed using accuracy, MCC, ROC, PR curves, AUC-ROC, AUC-PR, and LOOCV.
Main Results:
- The SBM-based SVM model achieved 80.00% accuracy and an AUC-ROC of 0.86 in identifying PD-MCI.
- The model demonstrated a Matthews Correlation Coefficient (MCC) of 0.56 and an AUC-PR of 0.89.
- Leave-one-out cross-validation (LOOCV) yielded a classification accuracy of 78.08%.
Conclusions:
- Surface-based morphometry combined with machine learning can effectively identify PD-MCI.
- This approach holds promise for the automatic diagnosis of PD-MCI.
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