预测 2 型糖尿病血管钙化风险的机器学习方法:回顾性研究

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Xue Liang, Xinyu Li, Guosheng Li, Bing Wang, Yudan Liu, Dongli Sun, Li Liu, Ran Zhang, Shukun Ji, Wanying Yan, Ruize Yu, Zhengnan Gao, Xuhan Liu
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引用次数: 0

摘要

背景:最近,2型糖尿病(T2DM)患者的血管钙化(VC)发生率更高、程度更严重,导致T2DM患者血管并发症的发生率和死亡率上升:构建并验证 T2DM 患者血管钙化风险预测模型:方法:从电子病历系统中提取 23 个基线人口统计学和临床特征。采用最小绝对缩减法和选择算子法筛选出10个临床特征,并基于8种机器学习(ML)算法(k-近邻算法[k-NN]、轻梯度提升机、逻辑回归算法[LR]、多层感知算法[(MLP]、奈夫贝叶斯算法[NB]、随机森林算法[RF]、支持向量机算法[SVM]、XGBoost算法[XGB])建立预测模型。使用接收者工作特征曲线下面积(AUC)、准确度和精确度评估模型性能:在训练集和测试集中分别回顾性地收集了1407名和352名患者。在八个模型中,NB 模型的 AUC 值高于其他模型(NB:0.753,LGB:0.719,LR:0.749,MLP:0.715,RF:0.722,SVM:0.689,XGB:0.707,P 结论:该研究建立了一个预测血管瘤的模型:本研究基于 ML 和 2 型糖尿病患者的临床特征建立了 VC 预测模型。NB 模型是一种有潜力帮助临床医生识别高危患者 VC 的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study

A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study

Background

Recently, patients with type 2 diabetes mellitus (T2DM) have experienced a higher incidence and severer degree of vascular calcification (VC), which leads to an increase in the incidence and mortality of vascular complications in patients with T2DM.

Hypothesis

To construct and validate prediction models for the risk of VC in patients with T2DM.

Methods

Twenty-three baseline demographic and clinical characteristics were extracted from the electronic medical record system. Ten clinical features were screened with least absolute shrinkage and selection operator method and were used to develop prediction models based on eight machine learning (ML) algorithms (k-nearest neighbor [k-NN], light gradient boosting machine, logistic regression [LR], multilayer perception [(MLP], Naive Bayes [NB], random forest [RF], support vector machine [SVM], XGBoost [XGB]). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and precision.

Results

A total of 1407 and 352 patients were retrospectively collected in the training and test sets, respectively. Among the eight models, the AUC value in the NB model was higher than the other models (NB: 0.753, LGB: 0.719, LR: 0.749, MLP: 0.715, RF: 0.722, SVM: 0.689, XGB:0.707, p < .05 for all). The k-NN model achieved the highest sensitivity of 0.75 (95% confidence interval [CI]: 0.633–0.857), the MLP model achieved the highest accuracy of 0.81 (95% CI: 0.767–0.852) and specificity of 0.875 (95% CI: 0.836–0.912).

Conclusions

This study developed a predictive model of VC based on ML and clinical features in type 2 diabetic patients. The NB model is a tool with potential to facilitate clinicians in identifying VC in high-risk patients.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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