血管性认知障碍早期预测机器学习模型的构建

doi:10.3969/j.issn.1006-9771.2021.09.011

Abstract

Abstract:

Objective To explore the predictive performance of machine learning model based on vascular risk factors in early prediction of vascular cognitive impairment.Methods From April to September, 2020, 70 subjects were enrolled and collected information of the demographics and vascular risk factors. They were assessed with Montreal Cognitive Assessment (MoCA), and then divided into normal group, vascular mild cognitive impairment (VaMCI) group and dementia group. The differences of vascular risk factors among the three groups were detected with one-way ANOVA, and the significant factors were selected to establish predictive models with support vector machine (SVM) and extreme learning machine (ELM). The predictive performance of two models was compared with Receiver Operating Characteristic Curve.Results There were 32 cases in the normal group, 23 in VaMCI group and 15 in dementia group. Systolic blood pressure, fasting blood glucose, total cholesterol, low density lipoprotein and blood uric acid were significantly different among the three groups (F > 3.318, P < 0.05). The area under curve was the most (0.911) in SVM model predicting for VaMCI (P < 0.01), and the predictive efficacy was better for SVM model.Conclusion SVM predictive model based on vascular risk factors may be more effective for predicting VaMCI.

Key words: vascular cognitive impairment, support vector machines, extreme learning machines, machine learning, predictive model

ZHANG Qian,BIAN Min-jie,HE Qin,HUANG Dong-feng. Machine Learning Models for Early Prediction of Vascular Cognitive Impairment[J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2021, 27(9): 1072-1077.

Figures/Tables 6

References 37

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变量	正常组 (n = 32)	VaMCI组 (n = 23)	痴呆组 (n = 15)	χ²/F值^a	P值^a	χ²/t值^b	P值^b	χ²/t值^c	P值^c
性别(男/女, n)	16/16	12/11	8/7	0.053	0.979
年龄(岁)	34.9±2.2	53.9±2.7	59.7±3.6	24.310	< 0.001	5.277	< 0.001	1.282	0.210
脑梗死(男/女, n)	0	5/6	6/4	0.444	0.505
脑出血(男/女, n)	0	2/2	2/3	0.090	0.764
非卒中(男/女, n)	16/16	5/3	0	0.401	0.527
教育年限(年)	13.6±0.7	9.4±0.9	10.1±1.0	7.965	< 0.001	3.631	< 0.001	0.519	0.608
身高(m)	1.6±0.01	1.6±0.02	1.7±0.02	0.453	0.638
体质量(kg)	62.5±2.4	65.1±2.6	64.6±1.8	0.348	0.708
BMI (kg/m²)	23.4±0.7	24.4±0.7	23.7±0.7	0.456	0.636

项目	正常组 (n = 32)	VaMCI组 (n = 23)	痴呆组 (n = 15)	F值^a	P值^a	t值^b	P值^b	t值^c	P值^c
总分	27.5±0.4	21.8±0.3	13.5±0.9	150.600	< 0.001	9.071	< 0.001	8.481	< 0.001
视空间与执行功能	4.6±0.1	3.8±0.3	1.6±0.3	41.430	< 0.001	2.720	0.010	4.783	< 0.001
命名功能	3.0±0.0	2.9±0.1	2.3±0.2	16.140	< 0.001	1.335	0.189	3.199	0.003
注意功能	6.0±0.0	4.4±0.2	2.8±0.3	90.480	< 0.001	10.040	< 0.001	4.298	< 0.001
语言功能	2.5±0.1	1.8±0.2	0.9±0.2	17.630	< 0.001	2.857	0.007	2.725	0.011
抽象功能	1.4±0.1	0.9±0.2	0.5±0.2	8.405	< 0.001	2.408	0.020	1.394	0.174
延迟回忆功能	3.8±0.2	2.2±0.3	0.5±0.3	41.230	<0.001	4.180	< 0.001	4.365	< 0.001
定向功能	5.9±0.1	4.8±0.2	4.1±0.4	16.140	< 0.001	5.746	< 0.001	1.383	0.1773

因素	正常组 (n = 32)	VaMCI组 (n = 23)	痴呆组 (n = 15)	F值^a	P值^a	t值^b	P值^b	t值^c	P值^c
收缩压(mmHg)	122.7±2.0	142.2±4.1	133.4±2.8	12.580	< 0.001	4.737	< 0.001	1.736	0.093
舒张压(mmHg)	77.1±1.4	80.6±1.4	78.4±1.6	1.333	0.272	1.588	0.112	1.041	0.306
空腹血糖(mmol/L)	5.2±0.2	6.5±0.3	6.2±0.4	8.925	< 0.001	4.488	< 0.001	0.743	0.464
总胆固醇(mmol/L)	4.1±0.2	4.8±0.3	4.135±0.2603	3.318	0.044	2.489	0.017	1.921	0.065
低密度脂蛋白(mmol/L)	3.1±0.1	3.3±0.3	2.3±0.2	6.644	0.003	0.786	0.436	2.846	0.008
血尿酸(μmol/L)	363.5±14.4	381.9±25.6	488.2±17.1	11.900	< 0.001	0.677	0.502	3.404	0.002

项目	SVM			ELM			P值
项目	正常组	VaMCI组	痴呆组	正常组	VaMCI组	痴呆组	P值
AUC	0.8662	0.9109	0.8723	0.8289	0.8634	0.8336	0.006
敏感性	0.7928	0.8068	0.8953	0.8837	0.8595	0.7653	0.953
特异性	0.8272	0.8583	0.7823	0.6389	0.7500	0.7823	0.212

Machine Learning Models for Early Prediction of Vascular Cognitive Impairment

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