Construction and validation of prediction model for diabetic distal symmetric polyneuropathy based on neural network_Chinese Journal of Evidence-Based Medicine

Authors：

JIANG Aijuan ¹ , WANG Lujie ¹ , LI Jiajie ¹ , LIN Yixuan ² , ZHAO Jindong ² , FANG Zhaohui ² ,  SHEN Guoming ¹

1. School of Integrated Traditional Chinese and Western Medicine, Anhui University of Traditional Chinese Medicine, Hefei 230012, P. R. China;
2. The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230031, P. R. China;

Corresponding author：

SHEN Guoming, Email: shengm_66@163.com

Keywords：

Diabetic neuropathy; Neural network algorithm; Prediction model; Characteristics of traditional Chinese medicine; Integration of traditional Chinese and Western medicine

DOI：

10.7507/1672-2531.202308003

Video：

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Objective To construct a prediction model of diabetics distal symmetric polyneuropathy (DSPN) based on neural network algorithm and the characteristic data of traditional Chinese medicine and Western medicine. Methods From the inpatients with diabetes in the First Affiliated Hospital of Anhui University of Chinese Medicine from 2017 to 2022, 4 071 cases with complete data were selected. The early warning model of DSPN was established by using neural network, and 49 indicators including general epidemiological data, laboratory examination, signs and symptoms of traditional Chinese medicine were included to analyze the potential risk factors of DSPN, and the weight values of variable features were sorted. Validation was performed using ten-fold crossover, and the model was measured by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and AUC value. Results The mean duration of diabetes in the DSPN group was about 4 years longer than that in the non-DSPN group (P<0.001). Compared with non-DSPN patients, DSPN patients had a significantly higher proportion of Chinese medicine symptoms and signs such as numbness of limb, limb pain, dizziness and palpitations, fatigue, thirst with desire to drink, dry mouth and throat, blurred vision, frequent urination, slow reaction, dull complexion, purple tongue, thready pulse and hesitant pulse (P<0.001). In this study, the DSPN neural network prediction model was established by integrating traditional Chinese and Western medicine feature data. The AUC of the model was 0.945 3, the accuracy was 87.68%, the sensitivity was 73.9%, the specificity was 92.7%, the positive predictive value was 78.7%, and the negative predictive value was 90.72%. Conclusion The fusion of Chinese and Western medicine characteristic data has great clinical value for early diagnosis, and the established model has high accuracy and diagnostic efficacy, which can provide practical tools for DSPN screening and diagnosis in diabetic population.

Citation： JIANG Aijuan, WANG Lujie, LI Jiajie, LIN Yixuan, ZHAO Jindong, FANG Zhaohui, SHEN Guoming. Construction and validation of prediction model for diabetic distal symmetric polyneuropathy based on neural network. Chinese Journal of Evidence-Based Medicine, 2024, 24(3): 265-271. doi: 10.7507/1672-2531.202308003 Copy

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2.	Nicolucci A, Romeo L, Bernardini M, et al. Prediction of complications of type 2 diabetes: a machine learning approach. Diabetes Res Clin Pract, 2022, 190: 110013.
3.	Williams BM, Borroni D, Liu R, et al. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study. Diabetologia, 2020, 63(2): 419-430.
4.	梁晓春. 中西医结合防治糖尿病周围神经病变的思路及其研究. 中国中西医结合杂志, 2019, 39(3): 263-266.
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12.	田晨, 杨秋玉, 赖鸿皓, 等. 诊断试验准确性比较研究的统计分析. 中国循证医学杂志, 2022, 22(12): 1474-1482.
13.	Almansour NA, Syed HF, Khayat NR, et al. Neural network and support vector machine for the prediction of chronic kidney disease: a comparative study. Comput Biol Med, 2019, 109: 101-111.
14.	Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of go without human knowledge. Nature, 2017, 550(7676): 354-359.
15.	Gholipour K, Asghari-Jafarabadi M, Iezadi S, et al. Modelling the prevalence of diabetes mellitus risk factors based on artificial neural network and multiple regression. East Mediterr Health J, 2018, 24(8): 770-777.
16.	Liu L, Wang M, Li G, et al. Construction of predictive model for type 2 diabetic retinopathy based on extreme learning machine. Diabetes Metab Syndr Obes, 2022, 15: 2607-2617.
17.	张玉玺, 贺松, 尤思梦. 集成学习在糖尿病预测中的应用. 智能计算机与应用, 2019, 9(5): 176-179.
18.	李军, 胡晓娟, 屠立平, 等. 基于舌象参数与多指标特征联合的2型糖尿病风险预测模型. 中国中医基础医学杂志, 2021, 27(3): 451-456, 501.
19.	李军, 胡晓娟, 周昌乐, 等. 基于随机森林算法的糖尿病舌象特征分析和诊断模型研究. 中华中医药杂志, 2022, 37(3): 1639-1643.
20.	叶倩云, 侯政昆, 刘凤斌. 中医病证结合模式下的风险预后研究. 中医杂志, 2018, 59(7): 550-553, 564.
21.	仝小林. 态靶医学—中医未来发展之路. 中国中西医结合杂志, 2021, 41(1): 16-18.
22.	江爱娟, 申国明, 尤良震, 等. 病证结合慢性疾病嵌套风险预测模型的构建与应用. 中华中医药杂志, 2022, 37(8): 4546-4549.
23.	谢佛荣, 景海龙, 邓菲菲. 人工智能的可解释性问题探析—基于因果关系视角. 南华大学学报(社会科学版), 2022, 23(4): 40-45.
24.	Feldman EL, Callaghan BC, Pop-Busui R, et al. Diabetic neuropathy. Nat Rev Dis Primers, 2019, 5(1): 42.
25.	中国医师协会中西医结合医师分会内分泌与代谢病学专业委员会. 糖尿病周围神经病变病证结合诊疗指南. 中医杂志, 2021, 62(18): 1648-1656.
26.	潘琦, 李洪梅, 邢小燕, 等. 北京地区13家医院门诊2型糖尿病患者周围神经病变危险因素分析和筛查方法评估. 北京医学, 2018, 40(11): 1009-1014.
27.	Ozaki K, Terayama Y, Matsuura T, et al. Effect of combined dyslipidemia and hyperglycemia on diabetic peripheral neuropathy in alloxan-induced diabetic WBN/Kob rats. J Toxicol Pathol, 2018, 31(2): 125-133.
28.	中国微循环学糖尿病与微循环专业委员会,中国医疗保健国际交流促进会基层卫生分会基层糖尿病学部,江苏省基层内分泌特色科室孵化联盟. 基层糖尿病微血管病变筛查与防治专家共识(2021年版). 中国医学前沿杂志(电子版), 2021, 13(6): 16-38.
29.	梁晓春. 糖尿病周围神经病变整合诊治进展. 中国中西医结合杂志, 2021, 41(6): 656-659.
30.	张雨楠, 朱涛, 曾维, 等. 基于机器学习的诊断性试验准确性研究(一): 研究设计. 中国循证医学杂志, 2023, 23(6): 725-730.

1. Elafros MA, Andersen H, Bennett DL, et al. Towards prevention of diabetic peripheral neuropathy: clinical presentation, pathogenesis, and new treatments. Lancet Neurol, 2022, 21(10): 922-936.
2. Nicolucci A, Romeo L, Bernardini M, et al. Prediction of complications of type 2 diabetes: a machine learning approach. Diabetes Res Clin Pract, 2022, 190: 110013.
3. Williams BM, Borroni D, Liu R, et al. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study. Diabetologia, 2020, 63(2): 419-430.
4. 梁晓春. 中西医结合防治糖尿病周围神经病变的思路及其研究. 中国中西医结合杂志, 2019, 39(3): 263-266.
5. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Eur Urol, 2015, 67(6): 1142-1151.
6. Debray TPA, Collins GS, Riley RD, et al. Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration. BMJ, 2023, 380: e071058.
7. 中华医学会糖尿病学分会. 中国2型糖尿病防治指南(2020年版). 中华糖尿病杂志, 2021, 13(4): 315-409.
8. 黎敬波, 马力. 中医临床常见症状术语规范(修订). 北京: 中国医药科技出版社, 2015.
9. 李灿东, 方朝义. 中医诊断学(5版). 北京: 中国中医药出版社, 2021.
10. 林逸轩, 汪敏生, 尤良震, 等. 基于不同维度统计学分析方法的糖尿病周围神经病变中医症状量表研制. 中国中医基础医学杂志, 2020, 26(10): 1511-1515.
11. da Costa NL, de Lima MD, Barbosa R. Evaluation of feature selection methods based on artificial neural network weights. Exp Sys App, 2021, 168: 114312.
12. 田晨, 杨秋玉, 赖鸿皓, 等. 诊断试验准确性比较研究的统计分析. 中国循证医学杂志, 2022, 22(12): 1474-1482.
13. Almansour NA, Syed HF, Khayat NR, et al. Neural network and support vector machine for the prediction of chronic kidney disease: a comparative study. Comput Biol Med, 2019, 109: 101-111.
14. Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of go without human knowledge. Nature, 2017, 550(7676): 354-359.
15. Gholipour K, Asghari-Jafarabadi M, Iezadi S, et al. Modelling the prevalence of diabetes mellitus risk factors based on artificial neural network and multiple regression. East Mediterr Health J, 2018, 24(8): 770-777.
16. Liu L, Wang M, Li G, et al. Construction of predictive model for type 2 diabetic retinopathy based on extreme learning machine. Diabetes Metab Syndr Obes, 2022, 15: 2607-2617.
17. 张玉玺, 贺松, 尤思梦. 集成学习在糖尿病预测中的应用. 智能计算机与应用, 2019, 9(5): 176-179.
18. 李军, 胡晓娟, 屠立平, 等. 基于舌象参数与多指标特征联合的2型糖尿病风险预测模型. 中国中医基础医学杂志, 2021, 27(3): 451-456, 501.
19. 李军, 胡晓娟, 周昌乐, 等. 基于随机森林算法的糖尿病舌象特征分析和诊断模型研究. 中华中医药杂志, 2022, 37(3): 1639-1643.
20. 叶倩云, 侯政昆, 刘凤斌. 中医病证结合模式下的风险预后研究. 中医杂志, 2018, 59(7): 550-553, 564.
21. 仝小林. 态靶医学—中医未来发展之路. 中国中西医结合杂志, 2021, 41(1): 16-18.
22. 江爱娟, 申国明, 尤良震, 等. 病证结合慢性疾病嵌套风险预测模型的构建与应用. 中华中医药杂志, 2022, 37(8): 4546-4549.
23. 谢佛荣, 景海龙, 邓菲菲. 人工智能的可解释性问题探析—基于因果关系视角. 南华大学学报(社会科学版), 2022, 23(4): 40-45.
24. Feldman EL, Callaghan BC, Pop-Busui R, et al. Diabetic neuropathy. Nat Rev Dis Primers, 2019, 5(1): 42.
25. 中国医师协会中西医结合医师分会内分泌与代谢病学专业委员会. 糖尿病周围神经病变病证结合诊疗指南. 中医杂志, 2021, 62(18): 1648-1656.
26. 潘琦, 李洪梅, 邢小燕, 等. 北京地区13家医院门诊2型糖尿病患者周围神经病变危险因素分析和筛查方法评估. 北京医学, 2018, 40(11): 1009-1014.
27. Ozaki K, Terayama Y, Matsuura T, et al. Effect of combined dyslipidemia and hyperglycemia on diabetic peripheral neuropathy in alloxan-induced diabetic WBN/Kob rats. J Toxicol Pathol, 2018, 31(2): 125-133.
28. 中国微循环学糖尿病与微循环专业委员会,中国医疗保健国际交流促进会基层卫生分会基层糖尿病学部,江苏省基层内分泌特色科室孵化联盟. 基层糖尿病微血管病变筛查与防治专家共识(2021年版). 中国医学前沿杂志(电子版), 2021, 13(6): 16-38.
29. 梁晓春. 糖尿病周围神经病变整合诊治进展. 中国中西医结合杂志, 2021, 41(6): 656-659.
30. 张雨楠, 朱涛, 曾维, 等. 基于机器学习的诊断性试验准确性研究(一): 研究设计. 中国循证医学杂志, 2023, 23(6): 725-730.

Chinese Journal of Evidence-Based Medicine

Construction and validation of prediction model for diabetic distal symmetric polyneuropathy based on neural network

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