[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127535-en":3,"doc-seo-127535-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127535,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Predicting metastasis in gastric cancer patients - machine learning-based approaches","Gastric cancer remains a leading cause of cancer-related mortality worldwide, with limited long-term survival. This study builds predictive machine learning models to estimate metastasis status in gastric cancer patients using demographic and clinical variables. Data from 733 patients are split into training and testing sets (8:2), and 5-fold cross validation evaluates six classifiers. Performance is assessed with F1 score, precision, sensitivity, specificity, ROC AUC, and PR-AUC. Overall results are optimized, with SVM and NN showing particularly strong AUC and sensitivity.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nPredicting metastasis in gastric cancer patients: machine learning‑based approaches  \nAtefeh Talebi1,2, Carlos A. Celis‑Morales2,3, Nasrin Borumandnia4*, Somayeh Abbasi5, Mohamad Amin Pourhoseingholi6, Abolfazl Akbari7 & JavadYousefi8  \nGastric cancer (GC), with a 5‑year survival rate of less than 40%, is known as the fourth principal reason of cancer‑related mortality over the world. This study aims to develop predictive models using different machine learning (ML) classifiers based on both demographic and clinical variables to predict metastasis status of patients with GC. The data applied in this study including 733 of GC patients, divided into a train and test groups at a ratio of 8:2, diagnosed atTaleghani tertiary hospital. In order to predict metastasis in GC, ML‑based algorithms, including Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (RT) and Logistic Regression (LR), with 5‑fold cross validation were performed. To assess the model performance, F1 score, precision, sensitivity, specificity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and precision‑recall AUC (PR‑AUC) were obtained. 262 (36%) experienced metastasis among 733 patients with GC. Although all models have optimal performance, the indices ofSVM model seems to be more appropiate (training set: AUC: 0.94, Sensitivity: 0.94; testing set: AUC: 0.85, Sensitivity:  \n0.92). Then, NN has the higher AUC among ML approaches (training set: AUC: 0.98; testing set: AUC: 0.86). The RF of ML‑based models, which determine size of tumor and age as two essential variables, is considered as the third efficient model, because of higher specificity andAUC (84% and 87%) . Based on the demographic and clinical characteristics, ML approaches can predict the metastasis status in GC patients. According toAUC, sensitivity and specificity in both SVM and NN can be regarded as better algorithms among 6 applied ML‑based methods.  \nAbbreviations  \nAUC Area under the curve  \nDT Decision tree GC Gastric cancer LR Logistic regression ML Machine learning NB Naïve Bayes  \nNN Neural network RF Random forest  \nROC Receiver operating characteristic curve SVM Support vector machine  \nGastric cancer (GC) is considered as third invasive malignant growth across the globe1. Incidence of GC may occur by genetic and environmental effects in developing countries2. Although the morbidity and mortality of GC have reduced over the past few decades in some nations, the malignancy has remained the fourth leading  \n1Colorectal Research Center, Iran University of Medical Center, Tehran, Iran. 2British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, UK. 3Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK. 4Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 5Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran. 6Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 7Colorectal Research Center, Iran University of Medical Sciences, Tehran, Iran. 8Department of Internal Medicine, Iran University of Medical Sciences, Tehran, Iran.* email: [borumand.n@gmail.com](borumand.n@gmail.com); [nasrin.borumand@sbmu.ac.ir](nasrin.borumand@sbmu.ac.ir)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \ncause of cancer-related deaths over the world3. The mortality of the cancer is also growing and it endangers people’s health among Iranian society4.  \nVarious automated computational processes enable machines to analysis data. Machine learning (ML) is a branch of artificial intelligence that serves a series of algorithms from training data. ML algorithms identify pa","cbCaiuqDaq8q4qpT","https://ap.wps.com/l/cbCaiuqDaq8q4qpT","pdf",2612984,1,12,"English","en",105,"# Introduction\n## Study motivation and background\n# Methods\n## Dataset and training/testing split\n## Machine learning classifiers and cross validation\n## Evaluation metrics\n# Results\n## Metastasis prevalence and model performance\n# Discussion\n## Comparative model effectiveness","[{\"question\":\"What machine learning classifiers are used to predict metastasis in gastric cancer patients?\",\"answer\":\"The study applies Naive Bayes, Random Forest, Support Vector Machine, Neural Network, Decision Tree, and Logistic Regression, evaluated with 5-fold cross validation.\"},{\"question\":\"How is the dataset prepared for model training and testing?\",\"answer\":\"Data include 733 gastric cancer patients, diagnosed at Taleghani tertiary hospital, divided into training and testing groups with an 8:2 ratio.\"},{\"question\":\"Which models perform best according to AUC and sensitivity?\",\"answer\":\"SVM shows strong performance, and NN has the higher AUC among the approaches; Random Forest is also highlighted as efficient, with tumor size and age as key variables.\"}]","Predicting metastasis in gastric cancer patients - machine learning-based approaches | PDF",1785939817,30,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"predicting-metastasis-in-gastric-cancer-patients-machine-learning-based-approaches","",{"@graph":36,"@context":86},[37,54,69],{"@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/predicting-metastasis-in-gastric-cancer-patients-machine-learning-based-approaches/127535/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What machine learning classifiers are used to predict metastasis in gastric cancer patients?","Question",{"text":76,"@type":77},"The study applies Naive Bayes, Random Forest, Support Vector Machine, Neural Network, Decision Tree, and Logistic Regression, evaluated with 5-fold cross validation.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the dataset prepared for model training and testing?",{"text":81,"@type":77},"Data include 733 gastric cancer patients, diagnosed at Taleghani tertiary hospital, divided into training and testing groups with an 8:2 ratio.",{"name":83,"@type":74,"acceptedAnswer":84},"Which models perform best according to AUC and sensitivity?",{"text":85,"@type":77},"SVM shows strong performance, and NN has the higher AUC among the approaches; 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