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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References(15)

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