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
{"title":"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","authors":"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","doi":"10.1002/clc.24264","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Hypothesis</h3>\n \n <p>To construct and validate prediction models for the risk of VC in patients with T2DM.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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 (<i>k</i>-nearest neighbor [<i>k</i>-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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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, <i>p</i> < .05 for all). The <i>k</i>-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).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":10201,"journal":{"name":"Clinical Cardiology","volume":"47 4","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/clc.24264","citationCount":"0","resultStr":"{\"title\":\"A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study\",\"authors\":\"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\",\"doi\":\"10.1002/clc.24264\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Hypothesis</h3>\\n \\n <p>To construct and validate prediction models for the risk of VC in patients with T2DM.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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 (<i>k</i>-nearest neighbor [<i>k</i>-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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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, <i>p</i> < .05 for all). The <i>k</i>-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).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>This study developed a predictive model of VC based on ML and clinical features in type 2 diabetic patients. 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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.
期刊介绍:
Clinical Cardiology provides a fully Gold Open Access forum for the publication of original clinical research, as well as brief reviews of diagnostic and therapeutic issues in cardiovascular medicine and cardiovascular surgery.
The journal includes Clinical Investigations, Reviews, free standing editorials and commentaries, and bonus online-only content.
The journal also publishes supplements, Expert Panel Discussions, sponsored clinical Reviews, Trial Designs, and Quality and Outcomes.