[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127641-en":3,"doc-seo-127641-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127641,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis - population-based study","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.","TYPE Original Research PUBLISHED 29 November 2023 DOI 10.3389/fcvm.2023.1296405  \nEDITED BY  \nAlessandro Polizzi,  \nUniversity of Catania, Italy  \nREVIEWED BY  \nLi-Da Wu,  \nNanjing Medical University, China Simona Santonocito,  \nUniversità degli Studi di Catania, Italy  \n*CORRESPONDENCE  \nYan Zhang  \n [18558750600@163.com](18558750600@163.com)  \nRECEIVED 18 September 2023  \nACCEPTED 17 November 2023  \nPUBLISHED 29 November 2023  \nCITATION  \nWang 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.  \nFront. Cardiovasc. Med. 10:1296405 .  \ndoi: 10.3389/fcvm.2023.1296405  \nCOPYRIGHT  \n© 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.  \nApplication of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk inpatients with periodontitis: a population-based study  \nYicheng 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*  \n1Department 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  \nBackground: 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.  \nMethods: 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.  \nResults: 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","cbCaivrJDtXAsGoE","https://ap.wps.com/l/cbCaivrJDtXAsGoE","pdf",6806047,1,12,"English","en",105,"# Background\n# Methods\n## Data source and study design\n## Model development and evaluation\n# Results\n# Conclusion","[{\"question\":\"What was the study’s goal?\",\"answer\":\"To construct and validate a machine learning prediction model estimating coronary heart disease risk among individuals with periodontitis.\"},{\"question\":\"What data source and time period were used?\",\"answer\":\"The study used NHANES data covering the years 2009 to 2014, including 3,245 participants with confirmed periodontitis.\"},{\"question\":\"Which evaluation metrics and algorithms were used?\",\"answer\":\"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.\"}]","Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis - 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