[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-328793-105":3,"detail-sidebar-cat-0-en-105":73,"doc-detail-328793-en":123},{"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":66,"head_meta":68,"extra_data":70,"updated_unix":72},105,"en","data-privacy-aware-machine-learning-approach-in-pancreatic-cancer-diagnosis","Data privacy-aware machine learning approach in pancreatic cancer diagnosis","","Pancreatic ductal adenocarcinoma (PDAC) is highly lethal when diagnosed at an advanced stage, with less than 10% five-year survival, whereas early diagnosis can raise five-year survival up to about 70%. The study addresses the need for a reliable, data privacy-aware machine learning method to diagnose pancreatic cancer using biomarkers while protecting confidentiality. It preprocesses urine biomarker data, encrypts it with the Fernet algorithm, evaluates ten ML models, and reports performance using F1, recall, precision, and accuracy.",{"@graph":14,"@context":65},[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/data-privacy-aware-machine-learning-approach-in-pancreatic-cancer-diagnosis/328793/",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/data-privacy-aware-machine-learning-approach-in-pancreatic-cancer-diagnosis/328793.png","ImageObject",300,407,{"name":42,"@type":43},"Levi","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-04","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",11,{"@type":57,"mainEntity":58},"FAQPage",[59],{"name":60,"@type":61,"acceptedAnswer":62},"Which biomarkers and models show strong diagnostic performance?","Question",{"text":63,"@type":64},"The LGBM algorithm achieves the highest accuracy (98.8%). Feature importance analysis highlights plasma_CA19_9, REG1A, TFF1, and LYVE1, with LIME used to interpret influential features in decision-making.","Answer","https://schema.org",{"og:url":32,"og:type":67,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":69,"canonical":32},"index,follow",{"doc_id":71,"site_id":7},328793,1789996601,{"code":4,"msg":74,"data":75},"success",[76,80,84,88,93,98,103,107,112,115,119],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":77,"show_sort_weight":78,"slug":79},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":81,"show_sort_weight":82,"slug":83},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Exam",70,"exam",{"id":89,"doc_module":4,"doc_module_name":25,"category_name":90,"show_sort_weight":91,"slug":92},5,"Comic",60,"comic",{"id":94,"doc_module":4,"doc_module_name":25,"category_name":95,"show_sort_weight":96,"slug":97},6,"Technology",50,"technology",{"id":99,"doc_module":4,"doc_module_name":25,"category_name":100,"show_sort_weight":101,"slug":102},7,"Healthcare",40,"healthcare",{"id":104,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":105,"slug":106},8,30,"research-report",{"id":108,"doc_module":4,"doc_module_name":25,"category_name":109,"show_sort_weight":110,"slug":111},9,"Religion & Spirituality",20,"religion-spirituality",{"id":110,"doc_module":4,"doc_module_name":25,"category_name":113,"show_sort_weight":110,"slug":114},"World Cup","world-cup",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":116,"slug":118},10,"Lifestyle","lifestyle",{"id":120,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":89,"slug":122},19,"General","general",{"code":4,"msg":74,"data":124},{"doc_id":71,"user_id":125,"nickname":42,"user_avatar":126,"doc_module":4,"category_id":104,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":127,"file_id":128,"file_url":129,"file_type":130,"file_size":131,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":132,"language":133,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":134,"faqs":135,"seo_title":136,"seo_description":12,"update_tm":137,"read_time":138,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","AKMEŞE  \nBMC Medical Informatics and Decision Making [https://doi.org/10.1186/s12911-024-02657-2](https://doi.org/10.1186/s12911-024-02657-2)  \n(2024) 24:248  \nBMC Medical Informatics and Decision Making  \n RESEARCH Open Access  \nData privacy-aware machine learning approach in pancreatic cancer diagnosis  \nÖmer Faruk AKMEŞE1*  \nAbstract  \nProblem Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The five-year survival rate after diagnosis is less than 10% . However, if diagnosed early, the five-year survival rate can reach up to 70% . Early diagnosis of PDAC can aid treatment and improve survival rates by taking necessary precautions. The challenge is to develop a reliable, data privacy-aware machine learning approach that can accurately diagnose pancreatic cancer with biomarkers.  \nAim The study aims to diagnose a patient’s pancreatic cancer while ensuring the confidentiality of patient records. In addition, the study aims to guide researchers and clinicians in developing innovative methods for diagnosing pancreatic cancer.  \nMethods Machine learning, a branch of artificial intelligence, can identify patterns by analyzing large datasets. The study pre-processed a dataset containing urine biomarkers with operations such as filling in missing values, cleaning outliers, and feature selection. The data was encrypted using the Fernet encryption algorithm to ensure confidentiality. Ten separate machine learning models were applied to predict individuals with PDAC. Performance metrics such as F1 score, recall, precision, and accuracy were used in the modeling process.  \nResults Among the 590 clinical records analyzed, 199 (33 . 7%) belonged to patients with pancreatic cancer, 208 (35 . 3%) to patients with non-cancerous pancreatic disorders (such as benign hepatobiliary disease), and 183 (31%) to healthy individuals. The LGBM algorithm showed the highest efficiency by achieving an accuracy of 98.8%. The accuracy of the other algorithms ranged from 98 to 86% . In order to understand which features are more critical and which data the model is based on, the analysis found that the features “plasma_CA19_ 9”, REG1A, TFF1, and LYVE1 have high importance levels. The LIME analysis also analyzed which features of the model are important in the decision-making process.  \nConclusions This research outlines a data privacy-aware machine learning tool for predicting PDAC. The results show that a promising approach can be presented for clinical application. Future research should expand the dataset and focus on validation by applying it to various populations.  \nKeywords Pancreas cancer, Diagnosis, Machine learning, Classification, Data privacy-aware  \n*Correspondence:  \nÖmer Faruk AKMEŞE[ofarukakmese@hitit.edu.tr](ofarukakmese@hitit.edu.tr)  \n1 Department of Computer Engineering, Hitit University Çorum,Çorum 19030, Türkiye  \nIntroduction  \nPancreatic cancer has high mortality rates due to its often advanced stage at diagnosis. The incidence of pancreatic cancer ranks last among the top ten cancers. Nonetheless, the prognosis for survival remains significantly poor [1]. In 2022, cancer statistics indicate that pancreatic cancer is the third leading cause of cancer-related deaths  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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 mate","cbCaibHx350kllw8","https://ap.wps.com/l/cbCaibHx350kllw8","pdf",1678503,14,"English","# Abstract\n## Problem and aim\n## Methods and evaluation\n## Results\n## Conclusions\n# Introduction\n## Background and mortality statistics\n## Need for early detection\n## Treatment context and diagnostic limitations","[{\"question\":\"Which biomarkers and models show strong diagnostic performance?\",\"answer\":\"The LGBM algorithm achieves the highest accuracy (98.8%). Feature importance analysis highlights plasma_CA19_9, REG1A, TFF1, and LYVE1, with LIME used to interpret influential features in decision-making.\"}]","Data privacy-aware machine learning approach in pancreatic cancer diagnosis | PDF",1789980994,35]