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Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining

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Listed:
  • Xin Luo
  • Jijia Sun
  • Hong Pan
  • Dian Zhou
  • Ping Huang
  • Jingjing Tang
  • Rong Shi
  • Hong Ye
  • Ying Zhao
  • An Zhang

Abstract

In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk complication combination was screened by association rules based on the Apriori algorithm. Risk factors were screened using the LASSO regression model, random forest model, and support vector machine. A risk prediction model was established using logistic regression analysis, and a dynamic nomogram was constructed. Receiver operating characteristic (ROC) curves, harrell’s concordance index (C-Index), calibration curves, decision curve analysis (DCA), and internal validation were used to evaluate the differentiation, calibration, and clinical applicability of the models. This study found that patients with T2DM had a high-risk combination of lower extremity vasculopathy, diabetic foot, and diabetic retinopathy. Based on this, body mass index, diastolic blood pressure, total cholesterol, triglyceride, 2-hour postprandial blood glucose and blood urea nitrogen levels were screened and used for the modeling analysis. The area under the ROC curves of the internal and external validations were 0.768 (95% CI, 0.744−0.792) and 0.745 (95% CI, 0.669−0.820), respectively, and the C-index and AUC value were consistent. The calibration plots showed good calibration, and the risk threshold for DCA was 30–54%. In this study, we developed and evaluated a predictive model for the development of a high-risk complication combination while uncovering the pattern of complications in patients with T2DM. This model has a practical guiding effect on the health management of patients with T2DM in community settings.

Suggested Citation

  • Xin Luo & Jijia Sun & Hong Pan & Dian Zhou & Ping Huang & Jingjing Tang & Rong Shi & Hong Ye & Ying Zhao & An Zhang, 2023. "Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-18, August.
    • Handle: RePEc:plo:pone00:0289749
    DOI: 10.1371/journal.pone.0289749
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    References listed on IDEAS

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    1. Andrew J. Vickers & Elena B. Elkin, 2006. "Decision Curve Analysis: A Novel Method for Evaluating Prediction Models," Medical Decision Making, , vol. 26(6), pages 565-574, November.
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