[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-125140-105":59,"doc-detail-125140-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","machine-learning-approaches-for-predicting-dental-caries-in-permanent-molars-of-children-and-adolescents-using-nhanes-2011-16-data-symposium-of-student-scholars-poster","Machine Learning Approaches for Predicting Dental Caries in Permanent Molars of Children and Adolescents Using NHANES 2011-16 Data - Symposium of Student Scholars Poster","","Dental caries persists as a common chronic condition in children and adolescents, impairing quality of life, educational performance, and school attendance. Analysis of NHANES data shows caries affecting 17.4% of ages 6–11 and 56.8% of ages 12–19, with higher prevalence among non-Hispanic Black and Mexican American youth and those from lower-income families. The study builds and evaluates machine learning models to predict DMFT status from demographic, dietary, and oral examination variables derived from NHANES 2011–2014.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-approaches-for-predicting-dental-caries-in-permanent-molars-of-children-and-adolescents-using-nhanes-2011-16-data-symposium-of-student-scholars-poster/125140/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-approaches-for-predicting-dental-caries-in-permanent-molars-of-children-and-adolescents-using-nhanes-2011-16-data-symposium-of-student-scholars-poster/125140.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-05","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of this study?","Question",{"text":112,"@type":113},"To develop a robust machine learning model that predicts DMFT presence in permanent molars of children and adolescents using NHANES demographic, dietary, and oral health data.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which data and methods were used to build the model?",{"text":117,"@type":113},"The study merged NHANES cycles 2011–2012 and 2013–2014, using demographic, oral examination, dietary behavior, and insurance information, and trained multiple algorithms including logistic regression, deep learning, XGBoost, support vector machines, and random forests.",{"name":119,"@type":110,"acceptedAnswer":120},"What factors were identified as significant predictors of dental caries?",{"text":121,"@type":113},"Preliminary results highlighted predictors such as income-to-poverty ratio, age, dietary sugar and carbohydrate intake, parental education levels, and race/ethnicity.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},125140,1785896880,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":24},5909887256941,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Kennesaw State University  \nDigitalCommons@Kennesaw State University  \nSymposium of Student Scholars  \nMachine Learning Approaches for Predicting Dental Caries in Permanent Molars of Children and Adolescents Using NHANES 2011-16 Data  \nPritam Deb  \nKennesaw State University  \nChristina Scherrer PhD Kennesaw State University  \nLin Li PhD  \nKennesaw State University  \nFollow this and additional works at: [https://digitalcommons.kennesaw.edu/undergradsymposiumksu](https://digitalcommons.kennesaw.edu/undergradsymposiumksu)  \n Part of the Biomedical Informatics Commons, Community Health and Preventive Medicine Commons, and the Dental Public Health and Education Commons  \nDeb, Pritam; Scherrer, Christina PhD; and Li, Lin PhD, \"Machine Learning Approaches for Predicting Dental Caries in Permanent Molars of Children and Adolescents Using NHANES 2011-16 Data\" (2024) . Symposium of Student Scholars. 11. [https://digitalcommons.kennesaw.edu/undergradsymposiumksu/fall2024/fall2024/1](https://digitalcommons.kennesaw.edu/undergradsymposiumksu/fall2024/fall2024/1)1  \nThis Poster is brought to you for free and open access by the Office of Undergraduate Research at DigitalCommons@Kennesaw State University. It has been accepted for inclusion in Symposium of Student Scholars by an authorized administrator of DigitalCommons@Kennesaw State University. For more information, please contact [digitalcommons@kennesaw.edu](digitalcommons@kennesaw.edu).  \nMachine Learning Approaches for Predicting Dental Caries in Permanent Molars of Children and Adolescents Using NHANES 2011-14 Data  \nPritam Deba, Christina Scherrerb, PhD, and Lin Lib, PhD, Kennesaw State University, GA  \nAbstract Text:  \nPurpose: Dental caries remains a prevalent chronic disease among children, significantly affecting their quality of life, educational outcomes, and school attendance. Between 2011 and 2016 , caries affected 17.4% of children aged 6-11 and 56.8% of adolescents aged 12-19, with higher incidences among non-Hispanic Black and Mexican American youth , and those from lower-income families. This study aims to develop a robust machine learning model to predict the presence of decayed, missing, or filled permanent molars (DMFT) in children and adolescents using demographic, dietary, and oral health examination data from the National Health and Nutrition Examination Survey (NHANES) for the years 2011 to 2014.  \nMethods/Approach: Our study utilized merged NHANES data from two cycles (2011- 2012 and 2013-2014), including demographic data, detailed oral examinations, dietary behavior records, and health insurance information for individuals aged 6 to 19. To develop the binary target variable, DMFT (“0”= no caries; “>0” = presence of caries) was constructed from the analysis of eight individual permanent molars. We employed a diverse array of machine learning algorithms—logistic regression, deep learning, XGBoost, support vector machines, and random forests—to enhance predictive accuracy and interpretability.  \nResults/Findings: Preliminary analysis identified significant predictors of dental caries, including income-to-poverty ratio, age, dietary sugar and carbohydrate intake, parental education levels, and race/ethnicity. The model's efficacy was assessed through metrics such as accuracy and area under the ROC curve. Detailed comparisons of the model performances will be presented to highlight the most effective models for DMFT predictions.  \nConclusion/Practical Implications: Machine learning models have proven effective for early detection of dental caries risks among children and adolescents using NHANES data. The results can inform healthcare providers in implementing targeted preventive measures to reduce caries prevalence and improve public health outcomes. Future work will enhance the model by incorporating additional questionnaire data from NHANES and behavioral factors to improve prediction accuracy.","cbCaifKpPxeOGfjg","https://ap.wps.com/l/cbCaifKpPxeOGfjg","pdf",169938,"English","# Abstract\n## Purpose\n## Methods/Approach\n## Results/Findings\n## Conclusion/Practical Implications","[{\"question\":\"What is the purpose of this study?\",\"answer\":\"To develop a robust machine learning model that predicts DMFT presence in permanent molars of children and adolescents using NHANES demographic, dietary, and oral health data.\"},{\"question\":\"Which data and methods were used to build the model?\",\"answer\":\"The study merged NHANES cycles 2011–2012 and 2013–2014, using demographic, oral examination, dietary behavior, and insurance information, and trained multiple algorithms including logistic regression, deep learning, XGBoost, support vector machines, and random forests.\"},{\"question\":\"What factors were identified as significant predictors of dental caries?\",\"answer\":\"Preliminary results highlighted predictors such as income-to-poverty ratio, age, dietary sugar and carbohydrate intake, parental education levels, and race/ethnicity.\"}]","Machine Learning Approaches for Predicting Dental Caries in Permanent Molars of Children and Adolescents Using NHANES 2011-16 Data - Symposium of Student Scholars Poster | PDF"]