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| Classification of Mild Cognitive Impairment, Alzheimer’s Disease, and Healthy ControlsBased on Weighted Random Forest-KNN |
| Yuan Yuyang1, Li Fangyun2, Li Yingjie1#*, Li Yunxia2#* |
1(School of Life Sciences, Shanghai University, Shanghai 200444, China)
2(Department ofNeurology,Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201399, China) |
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Abstract Mild cognitive impairment (MCI), a critical prodromal stage of Alzheimer's disease (AD), exhibits distinct neural oscillatory features from healthy controls (HC). These differences are pivotal for early AD diagnosis and research on cognitive aging mechanisms. Brain functional connectivity in the α-band (8~13 Hz) has been proven to be closely associated with resting-state network activity, yet its classification efficacy and key biomarkers across MCI, AD, and HC populations remain incompletely elucidated. Meanwhile, traditional machine learning algorithms inadequately assign weights to high-dimensional EEG features, making it difficult to capture subtle differences among multiple groups. This study employed a feature importance weighting strategy via random forest to enhance the discriminability of critical brain connections in traditional machine learning models, addressing the inefficient feature utilization of single models. Resting-state EEG data from 43 participants each in MCI, AD, and HC groups were analyzed. A full feature matrix (AFM) for the α-band was constructed using Pearson correlation coefficients (PCC), and brain connections with significant inter-group differences were filtered via analysis of variance (ANOVA) to form an effective feature matrix (EFM). An integrated weighted random forest-KNN (WRF-KNN) model was designed, and its performance in three-class (MCI/AD/HC) and two-class (MCI/AD) tasks was evaluated using 5-fold stratified cross-validation. Additionally, a network of critical brain connections was constructed based on the feature importance of random forest. The WRF-KNN model achieved an average accuracy of 93% ±2.1% in the three-class task, significantly outperforming single KNN (88% ± 3.5%) and random forest (90% ± 2.8%, P < 0.05); its accuracy in MCI-AD two-class classification reached 92%±1.8%. Concurrently, the model identified 20 critical α-band connections. Their importance scores revealed key differences in α-band neural oscillations and functional connectivity between MCI/AD patients and HC, MCI and AD groups exhibited reduced prefrontal-parietal connectivity and enhanced intra-occipital connectivity, whereas HC displayed a more uniform connection distribution. These findings open new perspectives for the early diagnosis of AD and research on cognitive aging.
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Received: 24 December 2025
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Corresponding Authors:
*E-mail: liyj@i.shu.edu.cn;doctorliyunxia@163.com
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| About author:: #Member,Chinese Society of Biomedical Engineering |
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