Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis - population-based study

Aug 5, 2026

A prediction model was developed and validated to estimate coronary heart disease risk in patients with periodontitis, reflecting growing recognition of links between periodontal inflammation and cardiovascular disease. Analyses used NHANES data from 2009–2014 covering 3,245 periodontitis-confirmed individuals, split into training and validation sets. Five machine learning models were compared using AUC, Brier score, calibration plots and decision curve analysis, with logistic regression used to create a nomogram.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Background

Methods

Data source and study design

Model development and evaluation

Results

Conclusion

TYPE Original Research PUBLISHED 29 November 2023 DOI 10.3389/fcvm.2023.1296405 EDITED BY Alessandro Polizzi, University of Catania, Italy REVIEWED BY Li-Da Wu, Nanjing Medical University, China Simona Santonocito, Università degli Studi di Catania, Italy *CORRESPONDENCE Yan Zhang [18558750600@163.com](18558750600@163.com) RECEIVED 18 September 2023 ACCEPTED 17 November 2023 PUBLISHED 29 November 2023 CITATION Wang Y, Ni B, Xiao Y, Lin Y, Jiang Y and Zhang Y (2023) Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a populationbased study. Front. Cardiovasc. Med. 10:1296405 . doi: 10.3389/fcvm.2023.1296405 COPYRIGHT © 2023 Wang, Ni, Xiao, Lin, Jiang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk inpatients with periodontitis: a population-based study Yicheng Wang1,2,3,4, Binghang Ni1,2,3,4, Yuan Xiao1,2,3,4, Yichang Lin1,2,3,4, Yu Jiang1,2,3,4 and Yan Zhang1,2,3,4* 1Department of Cardiovascular Medicine, Afﬁliated Fuzhou First Hospital of Fujian Medical University, Fuzhou, Fujian, China, 2The Third Clinical Medical College, Fujian Medical University, Fuzhou, Fujian, China, 3Cardiovascular Disease Research Institute of Fuzhou City, Fuzhou, Fujian, China, 4The Graduate School of Fujian Medical University, Fuzhou, Fujian, China Background: The association between periodontitis and cardiovascular disease is increasingly recognized. In this research, a prediction model utilizing machine learning (ML) was created and veriﬁed to evaluate the likelihood of coronary heart disease in individuals affected by periodontitis. Methods: We conducted a comprehensive analysis of data obtained from the National Health and Nutrition Examination Survey (NHANES) database, encompassing the period between 2009 and 2014.This dataset comprised detailed information on a total of 3,245 individuals who had received aconﬁrmed diagnosis of periodontitis. Subsequently, the dataset was randomly partitioned into a training set and a validation set at a ratio of 6:4 . As part of this study, we conducted weighted logistic regression analyses, both univariate and multivariate, to identify risk factors that are independent predictors for coronary heart disease in individuals who have periodontitis. Five different machine learning algorithms, namely Logistic Regression (LR), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Classiﬁcation and Regression Tree (CART), were utilized to develop the model on the training set. The evaluation of the prediction models’ performance was conducted on both the training set and validation set, utilizing metrics including AUC (Area under the receiver operating characteristic curve), Brier score, calibration plot, and decision curve analysis (DCA) . Additionally, a graphical representation called a nomogram was created using logistic regression to visually depict the predictive model. Results: The factors that were found to independently contribute to the risk, as determined by both univariate and multivariate logistic regression analyses, encompassed age, race, presence of myocardial infarction, chest pain status, utilization of lipid-lowering medications, levels of serum uric acid and serum creatinine. Among the ﬁve evaluated machine learning models, the KNN model exhibited exceptional accuracy, achieving an AUC v

FAQs

What was the study’s goal?

To construct and validate a machine learning prediction model estimating coronary heart disease risk among individuals with periodontitis.

What data source and time period were used?

The study used NHANES data covering the years 2009 to 2014, including 3,245 participants with confirmed periodontitis.

Which evaluation metrics and algorithms were used?

Model performance was assessed with AUC, Brier score, calibration plots, and decision curve analysis, and five algorithms were tested: LR, GBM, SVM, KNN, and CART.

Aug 5, 2026

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