[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-327167-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-327167-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","light-gradient-boosting-based-prediction-of-quality-of-life-among-oral-cancer-treated-patients","Light gradient boosting-based prediction of quality of life among oral cancer-treated patients","","Study examines how light gradient boosting models can forecast quality of life among patients treated for oral cancer, addressing major functional difficulties such as mouth opening and swallowing and the resulting later-stage presentation. Quality of life is influenced by risk factors, disease site, and treatment, making questionnaire-based prediction clinically relevant. Data derive from standardized EORTC QLQ-C30 and QLQ-HN43 assessments administered in Tamil and English, with follow-up after one year.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/light-gradient-boosting-based-prediction-of-quality-of-life-among-oral-cancer-treated-patients/327167/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/light-gradient-boosting-based-prediction-of-quality-of-life-among-oral-cancer-treated-patients/327167.png","ImageObject",300,407,{"name":42,"@type":43},"Lucas Martin","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-30","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What model approach was used to predict quality of life in oral cancer patients?","Question",{"text":63,"@type":64},"Light gradient boosting classifiers were used to predict quality of life based on questionnaire data and clinical inputs.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which quality-of-life instruments were administered to patients?",{"text":68,"@type":64},"The study used the EORTC QLQ-C30 and EORTC QLQ-HN43 questionnaires to capture multi-domain quality-of-life outcomes.",{"name":70,"@type":61,"acceptedAnswer":71},"What was the main performance outcome reported for the prediction model?",{"text":72,"@type":64},"The model reported 96% accuracy with a log loss of 0.20, indicating strong predictive performance.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},327167,1790030254,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":112,"language":139,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":12,"update_tm":143,"read_time":117,"preview_page_count":4},8796095360427,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Ramalingam etal. BMCOral Health (2024) 24:349 BMC Oral Health  \n[https://doi.org/10.1186/s12903-024-04050-x](https://doi.org/10.1186/s12903-024-04050-x)  \nRESEARCH Open Access  \nLight gradient boosting-based prediction of quality of life among oral cancer-treated patients  \nKarthikeyan Ramalingam 1, Pradeep Kumar Yadalam2*, Pratibha Ramani 1, Murugesan Krishna3, Salah Hafedh4*, Almir Badnjević5, Gabriele Cervino6 and Giuseppe Minervini7,8  \nAbstract  \nBackground and introduction Statisticians rank oral and lip cancer sixth in global mortality at 10. 2% . Mouth opening and swallowing are challenging. Hence, most oral cancer patients only report later stages. They worry about surviving cancer and receiving therapy. Oral cancer severely affects QOL. QOL is affected by risk factors, disease site, and treatment. Using oral cancer patient questionnaires, we use light gradient Boost Tree classifiers to predict life quality.  \nMethods DIAS records were used for 111 oral cancer patients. The European Organisation for Research and Treatment of Cancer’s QLQ-C30 and QLQ-HN43 were used to document the findings. Anyone could enroll, regardless of gender or age. The IHEC/SDC/PhD/OPATH-1954/19/TH-001 Institutional Ethical Clearance Committee approved this work. After informed consent, patients received the EORTC QLQ-C30 and QLQ-HN43 questionnaires. Surveys were in Tamil and English. Overall, QOL ratings covered several domains. We obtained patient demographics, case history, and therapy information from our DIAS (Dental Information Archival Software) . Enrolled patients were monitored for at least a year. After one year, the EORTC questionnaire was retaken, and scores were recorded. This prospective analytical exploratory study at Saveetha Dental College, Chennai, India, examined QOL at diagnosis and at least 12 months after primary therapy in patients with histopathologically diagnosed oral malignancies. We measured oral cancer patients’ quality of life using data preprocessing, feature selection, and model construction. A confusion matrix was created using light gradient boosting to measure accuracy.  \nResults Light gradient boosting predicted cancer patients’ quality of life with 96% accuracy and 0.20 log loss. Conclusion Oral surgeons and oncologists can improve planning and therapy with this prediction model. Keywords Quality of life, Oral cancer, Machine learning, EORTC, Clinical, A. I  \n*Correspondence:  \nPradeep Kumar Yadalam [pradeepkumar.sdc@saveetha.com](pradeepkumar.sdc@saveetha.com)[ ](pradeepkumar.sdc@saveetha.com)Salah Hafedh [hafedh.salah@gmc-ye.com](hafedh.salah@gmc-ye.com)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/l](http://creativecommons.org/l)icenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ([http://creativecommons.org/publicdomain/zero/1.0/](http://creativecommons.org/publicdomain/zero/1.0/)) applies to the data made available in this article, unless otherwise stated in a credit line to the data.  \nRamalingam et al. BMC Oral Health (2024) 24:349  \nBackground and introduction  \nIn the current global context, the most sought-after","cbCaiujjaxU7drDN","https://ap.wps.com/l/cbCaiujjaxU7drDN","pdf",1137311,"English","# Background and introduction\n# Methods\n# Results\n# Conclusion\n# Keywords","[{\"question\":\"What model approach was used to predict quality of life in oral cancer patients?\",\"answer\":\"Light gradient boosting classifiers were used to predict quality of life based on questionnaire data and clinical inputs.\"},{\"question\":\"Which quality-of-life instruments were administered to patients?\",\"answer\":\"The study used the EORTC QLQ-C30 and EORTC QLQ-HN43 questionnaires to capture multi-domain quality-of-life outcomes.\"},{\"question\":\"What was the main performance outcome reported for the prediction model?\",\"answer\":\"The model reported 96% accuracy with a log loss of 0.20, indicating strong predictive performance.\"}]","Light gradient boosting-based prediction of quality of life among oral cancer-treated patients | PDF",1789976584]