融合偏好学习和证据推理的疾病智能辅助诊断方法

doi:10.16381/j.cnki.issn1003-207x.2024.0608

Abstract

Abstract:

With the development of digital transformation in healthcare， intelligently analyzing the complex and diverse diagnosis and treatment information generated during patients' diagnosis and treatment processes， identifying key features that affect diseases， and accurately predicting patients' conditions are of great significance for assisting doctors in formulating diagnosis and treatment plans and providing treatment recommendations. Based on this， an intelligent auxiliary diagnosis method for diseases is proposed that integrates preference learning and evidential reasoning. Firstly， considering the differences in the impact of various disease features on diagnosis results， a preference learning model is established to obtain the weights of different features， thereby identifying the core feature set. Secondly， heterogeneous base classifiers are constructed based on different classification algorithms to realize the prediction of patients' conditions. Furthermore， to improve the accuracy of disease diagnosis， evidential reasoning is introduced to integrate and fuse the prediction results of the base classifiers， assisting doctors in judging patients' conditions. Finally， the feasibility and effectiveness of the method proposed in this paper are verified with actual medical datasets as examples.

Key words: feature selection, preference learning, evidential reasoning, ensemble learning, intelligent diagnosis

CLC Number:

C934

Xi Chen,Youqi Dou,Wenbo Zhang. An Intelligent Auxiliary Diagnosis Method for Diseases Integrating Preference Learning and Evidential Reasoning[J]. Chinese Journal of Management Science, 2026, 34(7): 177-188.

Figures/Tables 16

	$T j$ correct	$T j$ incorrect
$T i$ correct	$N 11$	$N 10$
$T i$ incorrect	$N 01$	$N 00$

特征符号	特征名称	特征符号	特征名称
$a 1$	年龄	$a 9$	病理学
$a 2$	性别	$a 10$	焦点
$a 3$	是否吸烟	$a 11$	风险
$a 4$	Hx吸烟	$a 12$	T指标
$a 5$	Hx放射性	$a 13$	N指标
$a 6$	甲状腺功能	$a 14$	M指标
$a 7$	体检	$a 15$	甲状腺癌阶段
$a 8$	腺病	$a 16$	放射响应效果

病历号	$a 1$	$a 2$	$a 3$	$a 4$	$a 5$	$a 6$	$a 7$	$a 8$
1	26	1	0	0	0	0	0	5
2	75	0	1	0	1	0	1	0
3	52	0	1	0	0	0	3	0
4	40	0	1	0	0	0	3	4
5	62	1	0	0	0	0	2	0
$a 9$	$a 10$	$a 11$	$a 12$	$a 13$	$a 14$	$a 15$	$a 16$	$a 17$
3	0	1	0	2	0	0	3	1
0	1	2	2	0	1	4	3	1
2	1	0	0	0	0	0	1	0
3	1	1	2	2	0	0	3	1
1	0	0	2	0	0	0	0	0

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算法	特征集	准确率	精确率	召回率	特异性	F1值
决策树	原始	0.9383	0.8946	0.8863	0.9587	0.8902
决策树	核心	0.9413	0.8973	0.8948	0.9595	0.8958
线性判别	原始	0.9521	0.9209	0.9083	0.9692	0.9145
线性判别	核心	0.9549	0.9278	0.9115	0.9720	0.9195
逻辑回归	原始	0.9375	0.8904	0.8885	0.9568	0.8892
逻辑回归	核心	0.9476	0.9143	0.8993	0.9666	0.9064
朴素贝叶斯	原始	0.9103	0.9159	0.7511	0.9728	0.8252
朴素贝叶斯	核心	0.9226	0.9220	0.7928	0.9736	0.8524
K近邻	原始	0.9347	0.9463	0.8150	0.9817	0.8755
K近邻	核心	0.9304	0.9185	0.8270	0.9711	0.8702

集成策略	特征集	准确率	精确率	召回率	特异性	F1值
平均法	原始	0.9413	0.9777	0.8102	0.9927	0.8860
平均法	核心	0.9420	0.9777	0.8130	0.9927	0.8877
投票法	原始	0.9465	0.9781	0.8287	0.9927	0.8972
投票法	核心	0.9493	0.9774	0.8398	0.9924	0.9034
ER集成	原始	0.9577	0.9528	0.8944	0.9825	0.9225
ER集成	核心	0.9621	0.9652	0.8981	0.9873	0.9303

特征数据集	决策树DT	线性判别LDA	逻辑回归LR	朴素贝叶斯NB	K近邻KNN
原始特征	0.1434	0.1377	0.2961	0.1692	0.2536
核心特征	0.1022	0.1866	0.2814	0.1968	0.2329

特征	A1	A2	A3	A4	A5	A6	A7
权重	0.0350	0.0919	0.1227	0.0219	0.0038	0.0008	0.0570
特征	A8	A9	A10	A11	A12	A13
权重	0.0000	0.1513	0.0747	0.0910	0.1519	0.1980

特征	A1	A2	A3	A4	A5	A6
权重	0.3973	0.1489	0.0382	0.0061	0.0000	0.1173
特征	A7	A8	A9	A10	A11	A12
权重	0.0000	0.2792	0.0000	0.0075	0.0054	0.0000

An Intelligent Auxiliary Diagnosis Method for Diseases Integrating Preference Learning and Evidential Reasoning

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胶质瘤	特征	A1	A2	A3	A4	A5	A6
	权重	0.0000	0.2657	0.0298	0.0000	0.0000	0.0000
	特征	A7	A8	A9	A10	A11	A12
	权重	0.2288	0.1287	0.0000	0.0640	0.0118	0.0317
	特征	A13	A14	A15	A16	A17	A18
	权重	0.0565	0.0000	0.0842	0.0000	0.0002	0.0101
	特征	A19	A20	A21	A22	A23
	权重	0.0000	0.0416	0.0000	0.0083	0.0387

数据集	特征	准确率	精确率	召回率	特异性	F1值
心脏病	原始	0.7822	0.7664	0.7554	0.8049	0.7609
心脏病	核心	0.8053	0.7857	0.7914	0.8171	0.7885
心力衰竭	原始	0.7358	0.6164	0.4688	0.8621	0.5325
心力衰竭	核心	0.8094	0.7349	0.6354	0.8916	0.6816
胶质瘤	原始	0.7355	0.6738	0.6362	0.8000	0.6544
胶质瘤	核心	0.8462	0.8177	0.8153	0.8686	0.8165

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