[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120584-en":3,"doc-seo-120584-105":29,"detail-sidebar-cat-0-en-105":89},{"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":11},120584,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Machine Learning-Based Liver Cancer Classification Using Gene Expression Microarray Data - Conference Paper","Early and accurate detection of liver tumors can save lives, since the liver is a vital, multifunctional organ. Supervised machine learning is used to support liver cancer diagnosis by learning from gene expression profiles derived from microarray data. The study emphasizes that larger sample sizes improve precision and reliability. Multiple datasets from the CuMiDa Database are combined and analyzed with RF, SVM, XGBoost, KNN, and decision tree classifiers. Results show accuracy and effect size increase with sample size while effect-size variance decreases, with RF reaching 96.55% accuracy.","Brought to you by INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA  \nBack  \nMachine Learning-Based Liver Cancer Classification Using Gene Expression Microarray Data  \nICETAS 2024-9th IEEE International Conference on Engineering Technologies and Applied Sciences • Conference Paper • 2024 • DOI: 10.1109/ICETAS62372.2024.11119847  Mahmoud, Amena a  ; Meraj, Syeda b  ; Saini, Shilpa c  ; Juneja, Sapna c ;  \nTalpur, Kazim Razad  ; +2 authors  \na Sophia University, Faculty of Science and Technology, Dept. of Information and Communication Sciences, Japan  \nShow all information  \nView PDF Full text  Export   Save to list  \nDocument Impact Cited by (0) References (25) Similar documents  \nAbstract  \nDetecting a liver tumor early and accurately can save lives because the liver is an important and multifunctional human organ. Machine learning algorithms have recently emerged as effective tools for enhancing liver cancer categorization using gene expression microarray data. This study proposes a supervised machine learning-based approach for liver cancer diagnosis that influences gene expression profiles to achieve an accurate diagnosis. A large sample size is crucial to be obtained and leads to a precise and reliable outcome. In this research, we combine multiple datasets from the Curated Microarray (CuMiDa) Database with the same features and use machine-learning models. Random forest (RF) model, SVM model, Xgboost model, K-nearest neighbor (KNN) model, and Decision tree (DT) model, and are used as classification models for classifying liver cancer using  \ngene expressions. The results indicate that effect size and classification accuracies increase, while variances in effect size shrink with the increase in sample size. The results reveal that the RF model has better accuracy of 96.55% . © 2024 IEEE.  \nAuthor keywords  \nGene Expression Microarray Bioinformatics; Liver Cancer Classification; Machine learning  \nIndexed keywords  \nEngineering controlled terms  \nBioinformatics; Classification (of information); Diagnosis; Diseases; Gene expression; Learning systems; Liver; Nearest neighbor search; Random forests; Supervised learning  \nEngineering uncontrolled terms  \nCancer classification; Effect size; Gene expression (microarray) data; Gene expression microarray; Gene expression microarray bioinformatic; Liver cancer classification; Liver cancers; Machinelearning; Random forest modeling; Sample sizes  \nEngineering main heading  \nDecision trees  \n© Copyright 2025 Elsevier B.V., All rights reserved.  \n Abstract  \nAuthor keywords Indexed keywords  \nAbout Scopus  \nWhat is Scopus Content coverage Scopus blog  \nScopus API Privacy matters  \nLanguage  \n⽇本語版を表⽰する查看简体中文版本  \n查看繁體中文版本  \nПросмотр версии на русском языке  \nCustomer Service  \nHelp Tutorials Contact us  \nTerms and conditions  Privacy policy  Cookies settings  \nAll content on this site: Copyright © 2025 Elsevier B.V. , its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies. For all open access content, the relevant licensing terms apply.  \nWe use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies  .","cbCaibYV2sd00bES","https://ap.wps.com/l/cbCaibYV2sd00bES","pdf",197265,1,3,"English","en",105,"# Abstract\n## Methodology\n## Models and results\n## Author and indexed keywords","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study focuses on early and accurate liver tumor detection and liver cancer diagnosis using gene expression microarray data.\"},{\"question\":\"Which machine learning models are used for classification?\",\"answer\":\"Random forest, SVM, XGBoost, K-nearest neighbor (KNN), and decision tree (DT) models are used to classify liver cancer based on gene expression.\"},{\"question\":\"How does sample size affect the outcomes?\",\"answer\":\"Increasing sample size raises classification accuracy and effect size while shrinking the variance of effect size.\"}]","Machine Learning-Based Liver Cancer Classification Using Gene Expression Microarray Data - Conference Paper | PDF",1785730770,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":28},"machine-learning-based-liver-cancer-classification-using-gene-expression-microarray-data-conference-paper","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":21},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/machine-learning-based-liver-cancer-classification-using-gene-expression-microarray-data-conference-paper/120584/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":23,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":63,"interactionType":64,"userInteractionCount":20},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"What problem does the study address?","Question",{"text":73,"@type":74},"The study focuses on early and accurate liver tumor detection and liver cancer diagnosis using gene expression microarray data.","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"Which machine learning models are used for classification?",{"text":78,"@type":74},"Random forest, SVM, XGBoost, K-nearest neighbor (KNN), and decision tree (DT) models are used to classify liver cancer based on gene expression.",{"name":80,"@type":71,"acceptedAnswer":81},"How does sample size affect the outcomes?",{"text":82,"@type":74},"Increasing sample size raises classification accuracy and effect size while shrinking the variance of effect size.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":90},[91,95,99,103,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":45,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]