Journal of Shandong University(Engineering Science) ›› 2023, Vol. 53 ›› Issue (4): 65-73.doi: 10.6040/j.issn.1672-3961.0.2023.025

• Machine Learning & Data Mining • Previous Articles     Next Articles

The classification of mild cognitive impairment based on supervised graph regularization and information fusion

Ying LI1(),Jiankun WANG2,*()   

  1. 1. School of Information Engineering, Shandong Management University, Jinan 250357, Shandong, China
    2. Shandong Big Data Center, Jinan 250011, Shandong, China
  • Received:2023-02-07 Online:2023-08-20 Published:2023-08-18
  • Contact: Jiankun WANG E-mail:liying200606@163.com;wangjk0203@163.com

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Abstract:

To precisely distinguish progressive and stable mild cognitive impairment (MCI). The projection matrix was learned from Alzheimer′s disease samples and normal control samples. The supervised graph regularization was used to optimize the local nearest neighbor relationship of the samples. Based on the projection matrix, the spatial transformation of the MCI samples was carried out to extract the discriminative features of progressive and stable MCI. The proposed features were fused with the scores of Mini-Mental State Examination and apolipoprotein E4. The SVM classifier was trained using the fused features for the MCI classification. The experiments were conduct on the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database. The classification accuracy reached to 73.33%. Compared with the existing approaches, the proposed method significantly improved the classification accuracy, sensitivity and specificity.

Key words: mild cognitive impairment, supervised graph regularization, information fusion, feature extraction, classification

CLC Number: 

  • TP391

Fig.1

The framework of the proposed method"

Table 1

Demographic and clinical information of the subjects"

样本 人数 年龄/岁临床痴呆评分[34]mini精神状态检查评分
男 女 平均 标准差 均值 标准差
NC 78 87 76.40 5.37 0 29.19 0.96
sMCI 63 32 74.94 7.32 0.5 27.69 1.73
pMCI 73 53 73.40 9.25 0.5 26.49 1.70
AD 72 70 76.10 7.51 0.5/1 23.20 2.01

Table 2

Verification of classification performance of SGRIP features"

特征提取方式 分类精度/% 敏感度/% 特异度/% Auc
CT特征+CV特征+无监督图[27] 69.37 75.39 61.23 0.695 1
CV特征+无监督图 67.21 73.01 59.46 0.702 8
CV特征+有监督图+自适应近邻结构优化 69.15 77.81 57.57 0.709 2
CV特征+有监督图+自适应近邻结构优化+特征选择(SGRIP特征) 70.24 78.76 58.64 0.700 9

Table 3

Verification of classification performance of concatenative features"

特征 分类精度/% 敏感度/% 特异度/% Auc
MMSE 62.44 53.27 74.65 0.658 3
APOE4 60.67 63.50 56.84 0.417 2
MMSE-APOE4 59.17 57.78 61.15 0.685 0
SGRIP特征 70.24 78.76 58.64 0.700 9
SGRIP特征-MMSE 71.99 78.23 63.63 0.733 3
SGRIP特征-APOE4 69.83 77.95 59.03 0.721 1
SGRIP特征-MMSE-APOE4 73.33 78.84 65.93 0.761 2

Table 4

Comparison with existing methods"

特征 分类器 分类精度/% 敏感度/% 特异度/% Auc
MFN特征[6] SVM(高斯核) 65.61 70.63 58.95 0.667 0
分级生物特征[13] SVM(线性核) 67.43 70.04 63.96 0.698 1
基于子空间学习的生物特征[27] SVM(线性核) 69.37 75.39 61.23 0.695 1
SGRIP特征-MMSE-APOE4 SVM(高斯核) 72.79 81.71 60.88 0.754 2
SGRIP特征-MMSE-APOE4 SVM(线性核) 73.33 78.84 65.93 0.761 2

Fig.2

Visualization of all MCI samples in the SGRIP feature subspace"

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