[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127929-en":3,"doc-seo-127929-105":30,"detail-sidebar-cat-0-en-105":95},{"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":4,"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},127929,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Lateral cephalometric parameters among Arab skeletal classes II and III patients and applying machine learning models - Research paper","The World Health Organization identifies malocclusion as a major oral health problem that affects patients’ health and well-being. This study develops machine learning models to classify individual Arab patients (Israel citizens) into skeletal class II or III. Using coded records of 502 patients diagnosed by Calculated_ANB, the research tests multiple ML models after PCA-based feature relevance selection, compares cephalometric parameters across groups, and evaluates correlations. Results highlight key parameter differences and a model achieving high classification accuracy.","Clinical Oral Investigations (2024) 28:511  \n[https://doi.org/10.1007/s00784-024-05900-2](https://doi.org/10.1007/s00784-024-05900-2)  \nRESEARCH  \nLateral cephalometric parameters among Arab skeletal classes II and III patients and applying machine learning models  \nKareem Midlej1 · Nezar Watted2,3,4 · Obaida Awadi2 · Samir Masarwa2 · Iqbal M. Lone1 · Osayd Zohud1 · Eva Paddenberg5 · Sebastian Krohn5 · Erika Kuchler6 · Peter Proff5 · Fuad A. Iraqi1,3,5  \nReceived: 27 March 2024 / Accepted: 20 August 2024 © The Author(s) 2024  \nAbstract  \nBackground The World Health Organization considers malocclusion one of the most essential oral health problems. This disease influences various aspects of patients’health and well-being. Therefore, making it easier and more accurate to understand and diagnose patients with skeletal malocclusions is necessary.  \nObjectives The main aim of this research was the establishment of machine learning models to correctly classify individual Arab patients, being citizens of Israel, as skeletal class II or III. Secondary outcomes of the study included comparing cephalometric parameters between patients with skeletal class II and III and between age and gender-specific subgroups, an analysis of the correlation of various cephalometric variables, and principal component analysis in skeletal class diagnosis. Methods This quantitative, observational study is based on data from the Orthodontic Center, Jatt, Israel. The experimental data consisted of the coded records of 502 Arab patients diagnosed as Class II or III according to the Calculated_ANB. This parameter was defined as the difference between the measured ANB angle and the individualized ANB ofPanagiotidis and Witt. In this observational study, we focused on the primary aim, i.e., the establishment of machine learning models for the correct classification of skeletal class II and III in a group of Arab orthodontic patients. For this purpose, various ML models and input data was tested after identifying the most relevant parameters by conducting a principal component analysis. As secondary outcomes this study compared the cephalometric parameters and analyzed their correlations between skeletal class II and III as well as between gender and age specific subgroups.  \nResults Comparison of the two groups demonstrated significant differences between skeletal class II and class III patients. This was shown for the parameters NL-NSL angle, PFH/AFH ratio, SNA angle, SNB angle, SN-Ba angle. SN-Pg angle, and ML-NSL angle in skeletal class III patients, and for S-N (mm) in skeletal class II patients. In skeletal class II and skeletal class III patients, the results showed that the Calculated_ANB correlated well with many other cephalometric parameters. With the help of the Principal Component Analysis (PCA), it was possible to explain about 71% of the variation between the first two PCs. Finally, applying the stepwise forward Machine Learning models, it could be demonstrated that the model works only with the parameters Wits appraisal and SNB angle was able to predict the allocation of patients to either skeletal class II or III with an accuracy of 0.95, compared to a value of 0.99 when all parameters were used (“general model”) . Conclusion There is a significant relationship between many cephalometric parameters within the different groups of gender and age. This study highlights the high accuracy and power of Wits appraisal and the SNB angle in evaluating the classification of orthodontic malocclusion.  \nKeywords Malocclusion · Class II · Class III · Cephalometric parameters · Disease classification  \nKareem Midlej and Nezar Watted contributed equally to this work.  \nExtended author information available on the last page of the article  \n1 3  \nIntroduction  \nThe World Health Organization (WHO) considers malocclusion one of the most essential oral health problems after caries and periodontal disease [1, 2] . Skeletal class II malocclusion (SCIIMO) accounts fo","cbCaipid98LlvFc6","https://ap.wps.com/l/cbCaipid98LlvFc6","pdf",2276832,1,16,"English","en",105,"# Abstract\n## Background and Objectives\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To build machine learning models that correctly classify individual Arab patients as skeletal class II or III.\"},{\"question\":\"What data and diagnostic criteria were used?\",\"answer\":\"The study uses coded records of 502 Arab patients diagnosed as class II or III according to the Calculated_ANB, derived from the measured ANB and individualized ANB.\"},{\"question\":\"Which parameters showed significant differences between skeletal class II and III?\",\"answer\":\"Significant differences were reported for NL-NSL angle, PFH/AFH ratio, SNA angle, SNB angle, SN-Ba angle, SN-Pg angle, and ML-NSL angle (class III), and S-N (mm) (class II).\"},{\"question\":\"How accurate were the machine learning classification models?\",\"answer\":\"A stepwise forward ML approach using Wits appraisal and SNB angle predicted class allocation with about 0.95 accuracy, compared with 0.99 when all parameters were used (the general model).\"}]","Lateral cephalometric parameters among Arab skeletal classes II and III patients and applying machine learning models - Research paper | PDF",1785943065,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"lateral-cephalometric-parameters-among-arab-skeletal-classes-ii-and-iii-patients-and-applying-machine-learning-models-research-paper","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/lateral-cephalometric-parameters-among-arab-skeletal-classes-ii-and-iii-patients-and-applying-machine-learning-models-research-paper/127929/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study?","Question",{"text":75,"@type":76},"To build machine learning models that correctly classify individual Arab patients as skeletal class II or III.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and diagnostic criteria were used?",{"text":80,"@type":76},"The study uses coded records of 502 Arab patients diagnosed as class II or III according to the Calculated_ANB, derived from the measured ANB and individualized ANB.",{"name":82,"@type":73,"acceptedAnswer":83},"Which parameters showed significant differences between skeletal class II and III?",{"text":84,"@type":76},"Significant differences were reported for NL-NSL angle, PFH/AFH ratio, SNA angle, SNB angle, SN-Ba angle, SN-Pg angle, and ML-NSL angle (class III), and S-N (mm) (class II).",{"name":86,"@type":73,"acceptedAnswer":87},"How accurate were the machine learning classification models?",{"text":88,"@type":76},"A stepwise forward ML approach using Wits appraisal and SNB angle predicted class allocation with about 0.95 accuracy, compared with 0.99 when all parameters were used (the general model).","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,123,126,131,134,138],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":29,"slug":122},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":124,"slug":125},30,"research-report",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},9,"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]