[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123324-en":3,"doc-seo-123324-105":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},123324,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Implementing Brain Tumor Detection Using Various Machine Learning Techniques","Brain tumor detection is critical because tumors arise from uncontrolled cell growth and delayed recognition can delay treatment. This study performs data collection and exploration, trains models using six machine learning methods, and evaluates performance with a confusion matrix. Experiments compare extreme gradient boosting (XGBoost), logistic regression, random forest, K-nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Random forest achieves the highest accuracy at 98.41%, outperforming the other evaluated classifiers.","Implementing brain tumor detection using various machine  \nlearning techniques  \nRani Puspita, Cindy Rahayu  \nComputer Science Department, School of Computer Science, Bina Nusantara University, Jakarta, Indonesia  \nArticle history:  \nReceived Jul 23, 2024 Revised Jan 4, 2025 Accepted Jan 16, 2025  \nKeywords:  \nBrain tumor Detection Evaluation Machine learning Random forest  \nCorresponding Author:  \nThe brain is a very complex organ of the human body. One of the brain diseases is a tumor. Brain tumors are caused by uncontrolled cell growth. Early recognition, classification and analysis of brain tumors is very important to find out whether there is a tumor in a person's brain so it is important for us to do this in order to treat the tumor thoroughly. Machine learning (ML) techniques that have the highest accuracy in detecting the health sector are extreme gradient boosting (XGBoost), logistic regression, random forest, k-nearest neighbor (KNN), naive Bayes, and support vector machine (SVM) . In this research, data collection and exploration were carried out, data training using six methods, and evaluation using a confusion matrix. After conducting the experiment, it was obtained that random forest had the highest accuracy, namely 98.41% . Where XGBoost obtained an accuracy of 98.14%, logistic regression obtained an accuracy of 97.34%, KNN and naive Bayes of 97.34%, and SVM of 97.88% .  \nThis is an open access article under the CC BY-SA license.  \nRani Puspita  \nComputer Science Department, School of Computer Science, Bina Nusantara University Jakarta, Indonesia 11480  \nEmail: [rani.puspita@binus.ac.id](rani.puspita@binus.ac.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe brain is a very complex human body organ. The brain has billions of cells. One of the diseases of the brain is a tumor. Brain tumors are caused by uncontrolled cell growth [1] . This is the same as research that states that there are all kinds of abnormalities in the brain that endanger human health. One of them is a brain tumor [2]. This can be supported by research conducted by Siar and Teshnehlab [3] that when most cells are old and damaged, they will be destroyed and replaced by newer cells. With that, problems arise and lead to tumor growth in the brain.  \nAccording to the World Health Organization (WHO), around 700,000 people are affected by brain tumors, and since 2019, around 86,000 patients have been diagnosed with brain tumors [4] . Of the 700,000, 69.1% of people were diagnosed with benign tumors and 30.1% were diagnosed with malignant tumors [5] . Tumors are malignant cells produced by the uncontrolled development of cancer cells. There are two types of tumors, namely malignant and benign. Malignant brain tumors originate in the brain, grow quickly, and aggressively attack the surrounding areas and affect the central nervous system. Then benign brain tumors is amass of cells that grow relatively slowly in the brain [6] .  \nThis is in line with other research which states that tumors are a serious cancer and can attack adults and children. Analysis and classification of brain tumors are very important to find out whether there really is a tumor in a person's brain so it is important for us to do this in order to treat the tumor adequately [7], and machine learning (ML) techniques are considered a good basis for carrying out classification and mining tasks. In ML, the features selected as input for the model have a good impact on the results of the model used [8] . The  \ndiagnosis of a brain tumor needs to be made clear by classifying it so that a positive brain tumor diagnosis can be treated immediately. In this case, the radiologist must diagnose the brain tumor as early as possible, and then check again to see if the results are correct [9] .  \nIn this way, the problem with this research is due to the lack of early detection of brain tumors, so many patients only find out that they actually suffer from a brain tumor. So, it is necessary to car","cbCaidT54hnSGeV5","https://ap.wps.com/l/cbCaidT54hnSGeV5","pdf",397600,1,10,"English","en",105,"# Abstract\n# Introduction\n## Brain tumor overview and importance of early detection\n## Machine learning background for classification\n# Related work\n# Methods and experimental setup\n# Results and evaluation","[{\"question\":\"Why is early brain tumor detection important?\",\"answer\":\"Brain tumors are caused by uncontrolled cell growth, and timely recognition and classification help determine whether a tumor exists so treatment can be planned appropriately.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"The research evaluates XGBoost, logistic regression, random forest, KNN, naive Bayes, and SVM, using the same training and evaluation approach.\"},{\"question\":\"What is the best-performing model and its accuracy?\",\"answer\":\"Random forest provides the highest accuracy of 98.41%, higher than XGBoost (98.14%), logistic regression (97.34%), KNN/naive Bayes (97.34%), and SVM (97.88%).\"}]","Implementing Brain Tumor Detection Using Various Machine Learning Techniques | PDF",1785815939,25,{"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},"implementing-brain-tumor-detection-using-various-machine-learning-techniques","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/implementing-brain-tumor-detection-using-various-machine-learning-techniques/123324/",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","2026-08-05","2026-08-04",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},"Why is early brain tumor detection important?","Question",{"text":76,"@type":77},"Brain tumors are caused by uncontrolled cell growth, and timely recognition and classification help determine whether a tumor exists so treatment can be planned appropriately.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are compared in the study?",{"text":81,"@type":77},"The research evaluates XGBoost, logistic regression, random forest, KNN, naive Bayes, and SVM, using the same training and evaluation approach.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the best-performing model and its accuracy?",{"text":85,"@type":77},"Random forest provides the highest accuracy of 98.41%, higher than XGBoost (98.14%), logistic regression (97.34%), KNN/naive Bayes (97.34%), and SVM (97.88%).","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]