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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

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.

Introduction

Diabetes mellitus (DM), a common chronic noncommunicable disease, is a metabolic disorder caused by dysfunction in the secretion or utilization of insulin in the human body. With the improvement in the living standard of the population, its prevalence is increasing, and DM is a major global health problem [1]. According to a report by the International Diabetes Federation, 537 million adults (aged 20–79 years) worldwide will have DM in 2021, with one in 10 people living with DM, and this number is expected to increase to 783 million by 2045. Global health spending on DM amounts to $966 billion and is expected to reach $1054 billion by 2045. Currently, DM is the ninth leading cause of death in humans, and complications from persistent hyperglycemia are an important cause of death and disability in patients, with more than four million adults dying from DM complications [2, 3]

Materials and method

This was a retrospective study. The sample data were obtained from the Zhangjiang area in the Pudong New Area of Shanghai, led by the School of Public Health of Shanghai University of Traditional Chinese Medicine. Moreover, with the assistance of Huamu, Jinyang, Yinxing, Siping, Sanlin, and Daqiao community health service centers in Pudong New Area, Shanghai, the project of “The fourth round of three-year action plan for public health construction of key disciplines of health education and promotion” was completed. Through questionnaire surveys and physical, biochemical, fundus, and peripheral nerve examinations, the project collected relevant data from community residents who participated in the community T2DM cohort project in six community health service centers from December 2015 to April 2016. The study has been ethically approved by the medical ethics committee of Longhua Hospital, Shanghai University of Traditional Chinese Medicine and in accordance with the Declaration of Helsinki ethical principles and guidelines

Results

After preprocessing, the data from the 810 sample cases were analyzed in two steps. The data containing complication conditions were first analyzed by association rules based on the Apriori algorithm, and the combination of whether or not a complication occurred (yes = 1, no = 0) was reincorporated into the sample data for modeling analysis based on identifying high-risk complication combinations.

Discussion

Among the common complications of DM, this study found that patients with T2DM had a higher risk of three complication combinations: lower extremity vascular disease, diabetic foot, and diabetic retinopathy. The algorithm results suggest that patients with T2DM have a 97.6% risk of diabetic retinopathy in the presence of lower extremity vascular disease and diabetic foot. Some of the findings suggest a correlation between the three T2DM complications of lower extremity vascular disease, diabetic foot, and diabetic retinopathy, with the three diseases affecting each other [38]. The prognosis and risk factors for the development of diabetic foot in patients with lower extremity vascular disease and diabetic retinopathy as one of the risk factors for lower extremity vascular disease [39] suggest that the results are consistent with the pathological basis for the development of DM complications. In patients with T2DM, lower extremity vascular disease is difficult to detect at an early stage, and as the disease further deteriorates, it will lead to adverse consequences, such as amputation of the patient’s limbs [40]. As a common complication of DM, diabetic foot is a major cause of disability, death, and increased medical burden for patients [41].

Conclusions

In this study, a high-risk complication combination in patients with T2DM was identified among 10 common diabetic complications by association rule analysis based on the Apriori algorithm (lower extremity vascular disease, diabetic foot, and diabetic retinopathy). Divide the study population based on whether the complication combination occurred, and six risk factors (BMI, DBP, GLU2H, TC, TG and BUN) for the complication combination were screened using three methods, LASSO regression analysis, RF and SVM and established predictive models. The model performance were evaluated by using ROC curves, calibration curves and decision curves in the training and validation sets, respectively, and good performance evaluation results in all aspects.

Citation: Luo X, Sun J, Pan H, Zhou D, Huang P, Tang J, et al. (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 18(8): e0289749. https://doi.org/10.1371/journal.pone.0289749

Editor: Yee Gary Ang, National Healthcare Group, SINGAPORE

Received: December 26, 2022; Accepted: July 26, 2023; Published: August 8, 2023

Copyright: © 2023 Luo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This study was supported by a grant from Shanghai Municipal Health Commission. (The grant number is 15GWZK1002). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

 

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0289749#ack

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