Predictive Modeling of Chronic Kidney Disease with Hypertension or Diabetes Based on Machine Learning Algorithms

Huijuan ZENG; Bo TIAN; Hongling YUAN; Jie HE; Guanxi LI; Guojia RU; Min XU; Dong ZHAN

doi:10.12259/j.issn.2095-610X.S20240315

Volume 45 Issue 3

Mar. 2024

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Article Navigation > Journal of Kunming Medical University > 2024 > 45(3): 99-105

Huijuan ZENG, Bo TIAN, Hongling YUAN, Jie HE, Guanxi LI, Guojia RU, Min XU, Dong ZHAN. Predictive Modeling of Chronic Kidney Disease with Hypertension or Diabetes Based on Machine Learning Algorithms[J]. Journal of Kunming Medical University, 2024, 45(3): 99-105. doi: 10.12259/j.issn.2095-610X.S20240315

Citation:

Huijuan ZENG, Bo TIAN, Hongling YUAN, Jie HE, Guanxi LI, Guojia RU, Min XU, Dong ZHAN. Predictive Modeling of Chronic Kidney Disease with Hypertension or Diabetes Based on Machine Learning Algorithms[J]. Journal of Kunming Medical University, 2024, 45(3): 99-105. doi: 10.12259/j.issn.2095-610X.S20240315

Citation:

Huijuan ZENG, Bo TIAN, Hongling YUAN, Jie HE, Guanxi LI, Guojia RU, Min XU, Dong ZHAN. Predictive Modeling of Chronic Kidney Disease with Hypertension or Diabetes Based on Machine Learning Algorithms[J]. Journal of Kunming Medical University, 2024, 45(3): 99-105. doi: 10.12259/j.issn.2095-610X.S20240315

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Predictive Modeling of Chronic Kidney Disease with Hypertension or Diabetes Based on Machine Learning Algorithms

doi: 10.12259/j.issn.2095-610X.S20240315

1.
Dept. of Nephrology，1st Affiliated Hospital of Kunming Medical University
2.
Dept. Scientific Research，Yunnan 1st People’s Hospital
3.
School of Basic Medical Sciences，Kunming Medical University，Kunming Yunnan 650500，China

Received Date: 2023-12-13
Available Online: 2024-03-11
Publish Date: 2024-03-30

Abstract

Abstract

Objective To build the early predictive model for chronic kidney disease （CKD） in hypertension and diabetes patients in the community. Methods The CKD patients were recruited from 4 health care centers in 4 urban areas in Kunming. The control group was residents without hypertension and diabetes （n = 1267）. The disease group was residents with hypertension and/or diabetes （n = 566）. The questionnaire survey, physical examination, laboratory testing, and 5 SNPs gene types in the PVT1 gene. The risk factors, which were filtered with logistics regression, were used to build predictive models. Four machine learning algorithms were built: support vector machine （SVM）, random forest （RF）, Naïve Bayes （NB）, and artificial neural network （ANN） models. Results Thirteen indicators included in the final diagnostic model: age, disease type, ethnicity, blood urea nitrogen, creatinine, eGFR from MDRD, ACR, eGFR from EPI2009, PAM13 score, sleep quality survey, staying-up late, PVT1 SNP rs11993333 and rs2720659. The accuracy, specificity, Kappa value, AUC of ROC, and PRC of ANN are greater than those of the other 3 models. The sensitivity of RF is the highest among 4 types of machine learning. Conclusions The ANN predictive model has a good ability of efficiency and classification to predict CKD with hypertension and/or diabetes patients in the community.
- Chronic kidney disease,
- Machine learning,
- Predictive modeling,
- Hypertension,
- Diabetes

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[1]	陈婷,邓云蕾,龚蓉. 终末期肾病合并感染的生物标志物检测意义及研究进展[J]. 临床肾脏病杂志,2022,22(3):243-247.
[2]	Santos M,Yin H,Steffick D,et al. Predictors of kidney function recovery among incident ESRD patients[J]. BMC Nephrol,2021,22(1):142-153. doi: 10.1186/s12882-021-02345-7
[3]	Bikbov B,Purcell C,Levey A,et al. Global,regional,and national burden of chronic kidney disease,1990-2017: A systematic analysis for the Global Burden of Disease Study 2017[J]. Lancet (London,England),2020,395(10225):709-733. doi: 10.1016/S0140-6736(20)30045-3
[4]	王仕鸿,令垚,杨子华,等. 基于时间序列模型的中国2020—2029年慢性肾病发病和患病情况预测研究[J]. 中国慢性病预防与控制,2023,31(11):801-806.
[5]	Zhang L,Wang F,Wang L,et al. Prevalence of chronic kidney disease in China: A cross-sectional survey[J]. Lancet (London,England),2012,379(9818):815-822. doi: 10.1016/S0140-6736(12)60033-6
[6]	郑旭彤,张曼,秦朱珠,等. 慢性肾病患者肾脏替代治疗辅助决策工具开发与验证研究的范围综述[J]. 中华护理教育,2023,20(4):500-507. doi: 10.3761/j.issn.1672-9234.2023.04.021
[7]	高翔,梅长林. 慢性肾脏病筛查诊断及防治指南[J]. 中国实用内科杂志,2017,37(1):28-34.
[8]	Zeng H,Jiang R,Zhou M,et al. Measuring patient activation in Chinese patients with hypertension and/or diabetes: Reliability and validity of the PAM13[J]. J Int Med Res,2019,47(12):5967-5976. doi: 10.1177/0300060519868327
[9]	Chen T,Li X,Li Y,et al. Prediction and risk stratification of kidney outcomes in IgA nephropathy[J]. Am J Kidney Dis,2019,74(3):300-309. doi: 10.1053/j.ajkd.2019.02.016
[10]	Chen Z,Zhang X,Zhang Z. Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models[J]. Int Urol Nephrol,2016,48(12):2069-2675. doi: 10.1007/s11255-016-1346-4
[11]	Polat H,Danaei Mehr H,Cetin A. Diagnosis of chronic kidney disease based on support vector machine by feature selection methods[J]. J Med Syst,2017,41(4):55-66. doi: 10.1007/s10916-017-0703-x
[12]	Almansour A,Syed F,Khayat R,et al. Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study[J]. Comput Biol Med,2019,109(6):101-111.
[13]	Wolfgram F,Garcia K,Evans G,et al. Association of albuminuria and estimated glomerular filtration rate with functional performance measures in older adults with chronic kidney disease[J]. Am J Nephrol,2017,45(2):172-179. doi: 10.1159/000455388
[14]	Zhang L,Zuo L,Xu G,et al. Community-based screening for chronic kidney disease among populations older than 40 years in Beijing[J]. Nephrology,dialysis,transplantation:official publication of the European Dialysis and Transplant Association - European Renal Association,2007,22(4):1093-1099. doi: 10.1093/ndt/gfl763
[15]	Wouters J,O'donoghue J,Ritchie J,et al. Early chronic kidney disease: Diagnosis,management and models of care[J]. Nature Reviews Nephrology,2015,11(8):491-502. doi: 10.1038/nrneph.2015.85

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