Chinese Journal of Tissue Engineering Research ›› 2023, Vol. 27 ›› Issue (2): 287-292.doi: 10.12307/2022.1016

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Predicting vascular mild cognitive impairment based on vascular risk factors: construction and application of a support vector machine model

Zhang Qian, Bian Minjie, He Qin, Huang Dongfeng   

  1. The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, Guangdong Province, China
  • Received:2022-01-05 Accepted:2022-02-11 Online:2023-01-18 Published:2022-06-20
  • Contact: Huang Dongfeng, Master, Chief physician, Professor, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, Guangdong Province, China
  • About author:Zhang Qian, Master, Physician, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, Guangdong Province, China
  • Supported by:
    the Clinical Medicine Research Program 5010 of Sun Yat-sen University, No. 2014001 (to HDF)

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Abstract: BACKGROUND: With the aging of the population, the number of patients with mild cognitive impairment is gradually increased. Increasing evidence has shown that vascular cognitive impairment has a significant correlation with vascular risk factors. Therefore, vascular risk factors can be used to identify and predict vascular cognitive impairment. 
OBJECTIVE: To explore the clinical value of support vector machine model in the recognition of vascular mild cognitive impairment (VaMCI) based on vascular risk factors, which is expected to be used as a screening tool in grassroots institutions, communities and home rehabilitation.
METHODS: Participants enrolled in the study were assessed for cognitive function and then divided into three groups: normal group, VaMCI group, and dementia group. Vascular risk factors with statistically significant differences were selected by analysis of variance among the three groups. The factors mentioned above were used to build the support vector machine model for screening VaMCI. Sensitivity, specificity, positive predictive value, negative predictive value, and area under curve were used to evaluate the predictive performance of the model.
RESULTS AND CONCLUSION: In the study, 80 participants enrolled were assessed for cognitive function and then divided into three groups: normal group (39 cases), VaMCI group (24 cases), and dementia group (17 cases). Systolic blood pressure, fasting blood glucose, total cholesterol, low-density lipoprotein and blood uric acid were observed to be significantly different among the three groups (P < 0.05) and they were therefore used to construct the prediction model. The sensitivity, specificity, positive predictive value, and negative predictive value of screening for VaMCI were 0.845 3, 0.919 4, 0.818 0, and 0.932 7, respectively and the area under the receiver operating curve was 0.892 3. To conclude, the support vector machine model has high diagnostic value in VaMCI screening based on vascular risk factors, and it is simple and easy to operate and can be applied to grassroots institutions, communities and home rehabilitation.

Key words: vascular risk factor, vascular mild cognitive impairment, dementia, support vector machines, machine learning

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