[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125162-en":3,"doc-seo-125162-105":30,"detail-sidebar-cat-0-en-105":91},{"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},125162,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Efficacy of automated machine learning models and feature engineering for diagnosis of equivocal appendicitis using clinical and computed tomography findings","Study evaluates diagnostic performance of automated machine learning using AutoGluon with automated feature engineering and selection (autofeat) for equivocal acute appendicitis in adults. Retrospective analysis covers 303 patients with indeterminate CT findings, split into appendicitis (n=115) and non-appendicitis (n=188). Models are compared by AUROC and metrics including accuracy, sensitivity, specificity, PPV, NPV, and F1 against conventional machine learning and the Adult Appendicitis Score. Combining clinical and CT data improves discrimination.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nEfficacy of automated machine learning models and feature engineering for diagnosis of equivocal appendicitis using clinical and computed tomography findings  \nJuho An1,8, Il Seok Kim2,8, Kwang‑Ju Kim 3, Ji Hyun Park4, Hyuncheol Kang5, Hyuk Jung Kim6, Young Sik Kim7 & Jung Hwan Ahn1,3*  \nThis study evaluates the diagnostic efficacy of automated machine learning (AutoGluon) with automated feature engineering and selection (autofeat), focusing on clinical manifestations, anda model integrating both clinical manifestations and CT findings in adult patients with ambiguous computed tomography (CT) results for acute appendicitis (AA). This evaluation was compared with conventional single machine learning models such as logistic regression(LR) and established scoring systems such as the Adult Appendicitis Score(AAS) to address the gap in diagnostic approaches for uncertain AA cases. In this retrospective analysis of 303 adult patients with indeterminate CT findings, the cohort was divided into appendicitis (n = 115) and non‑appendicitis (n = 188) groups. AutoGluon and autofeat were used for AA prediction. The AutoGluon‑clinical model relied solely on clinical data, whereas the AutoGluon‑clinical‑CT model included both clinical and CT data. The area under the receiver operating characteristic curve (AUROC) and other metrics for the test dataset, namely accuracy, sensitivity, specificity, PPV, NPV, and F1 score, were used to compare AutoGluon models with single machine learning models and theAAS. The single ML models in this study were LR, LASSO regression, ridge regression, support vector machine, decision tree, random forest, and extreme gradient boosting. Feature importance values were extracted using the “feature_importance”attribute from AutoGluon. The AutoGluon‑clinical model demonstrated anAUROC of 0.785 (95% CI  \n0.691–0.890), and the ridge regression model with only clinical data revealed anAUROC of 0.755 (95% CI 0.649–0.861). The AutoGluon‑clinical‑CT model (AUROC 0.886 with 95% CI 0.820–0.951) performed better than the ridge model using clinical and CT data (AUROC 0.852 with 95% CI 0.774–0.930,  \np = 0.029). A new feature, exp(‑(duration from pain to CT)3 + rebound tenderness), was identified (importance = 0.049, p = 0.001). AutoML (AutoGluon) and autoFE (autofeat) enhanced the diagnosis of uncertain AA cases, particularly when combining CT and clinical findings. This study suggests the potential of integrating AutoML and autoFE in clinical settings to improve diagnostic strategies  \n1Department of Emergency Medicine, Ajou University School of Medicine, World Cup-ro, Suwon, Gyeonggi-do 16499, South Korea. 2Department of Anesthesiology and Pain Medicine, Kangdong Sacred Hospital, Hallym University College of Medicine, Seongan-ro, Seoul 05355, South Korea. 3Electronics and Telecommunications Research Institute (ETRI), Techno sunhwan-ro, Daegu 42994, South Korea. 4Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for Innovative Medicine, Ajou University Medical Center, World Cup-ro, Suwon, Gyeonggi-do 16499, South Korea. 5Department of Big Data and AI, Hoseo University, Hoseo-ro, Asan, Chungcheongnam-do 31499, South Korea. 6Department of Radiology, Daejin Medical Center, Bundang Jesaeng General Hospital, Seohyeon-ro, Seongnam, Gyeonggi-do 13590, South Korea. 7Department of Emergency Medicine, Daejin Medical Center, Bundang Jesaeng General Hospital, Seohyeon-ro, Seongnam, Gyeonggi-do 13590, South Korea. 8These authors contributed equally: Juho An and Il  \nSeok Kim.* email: [erdrajh@naver.com](erdrajh@naver.com)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \nand patient outcomes and make more efficient use of healthcare resources. Moreover, this research supports further exploration of machine learning in diagnostic processes.  \nKeywords Acute appendicitis, Computed tomography, Automa","cbCaipj6cHZ2myhY","https://ap.wps.com/l/cbCaipj6cHZ2myhY","pdf",1278496,1,13,"English","en",105,"# Introduction\n## Background and diagnostic challenge of equivocal CT\n# Methods\n## Study design and patient cohort\n## AutoML models and feature engineering approach\n## Comparison models and evaluation metrics\n# Results\n## AUROC and performance metrics\n## Identified feature contributions\n# Discussion\n## Implications for clinical diagnosis and resource use","[{\"question\":\"What problem does the study address in diagnosing acute appendicitis?\",\"answer\":\"It targets diagnostic uncertainty when computed tomography results are equivocal, where clinicians need additional tools to distinguish appendicitis from other conditions.\"},{\"question\":\"How were patients selected and divided in the analysis?\",\"answer\":\"The retrospective dataset includes 303 adults with indeterminate CT findings, divided into an appendicitis group (n=115) and a non-appendicitis group (n=188).\"},{\"question\":\"What model setup produced the best diagnostic performance?\",\"answer\":\"The AutoGluon model combining clinical and CT findings (AutoGluon-clinical-CT) achieved higher AUROC than models using only clinical data or ridge regression with both data types.\"}]","Efficacy of automated machine learning models and feature engineering for diagnosis of equivocal appendicitis using clinical and computed tomography findings | PDF",1785897076,33,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"efficacy-of-automated-machine-learning-models-and-feature-engineering-for-diagnosis-of-equivocal-appendicitis-using-clinical-and-computed-tomography-findings","",{"@graph":36,"@context":85},[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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/efficacy-of-automated-machine-learning-models-and-feature-engineering-for-diagnosis-of-equivocal-appendicitis-using-clinical-and-computed-tomography-findings/125162/",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],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in diagnosing acute appendicitis?","Question",{"text":75,"@type":76},"It targets diagnostic uncertainty when computed tomography results are equivocal, where clinicians need additional tools to distinguish appendicitis from other conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were patients selected and divided in the analysis?",{"text":80,"@type":76},"The retrospective dataset includes 303 adults with indeterminate CT findings, divided into an appendicitis group (n=115) and a non-appendicitis group (n=188).",{"name":82,"@type":73,"acceptedAnswer":83},"What model setup produced the best diagnostic performance?",{"text":84,"@type":76},"The AutoGluon model combining clinical and CT findings (AutoGluon-clinical-CT) achieved higher AUROC than models using only clinical data or ridge regression with both data 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