[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124125-en":3,"doc-seo-124125-105":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},124125,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Optimizing Brain Tumor Prediction - A Comparative Study Of Machine Learning Algorithms","The prediction of brain tumors using machine learning has become a pivotal area in medical diagnostics, enabling enhanced early detection and more informed treatment planning. This study evaluates eight machine learning models for predicting brain tumors from medical clinical data, combining feature selection, normalization, and careful splits into training, validation, and test sets. Performance is assessed using precision, accuracy, recall, and F1 score. Results show Naïve Bayes delivers the best accuracy (53.8%) with balanced precision, recall, and F1, while SVM yields the lowest values, supporting feasibility of algorithmic identification and categorization. Further studies should examine additional data characteristics.","Keywords: Machine Learning, AdaBoost, K-Nearest Neighbors (KNN), Model Optimization, Classification Algorithms, data Preprocessing; Multilayer Perceptron  \nJournal Info:  \nSubmitted: November 28, 2024 Accepted:  \nDecember 18, 2024 Published:  \nDecember 31, 2024  \nOptimizing Brain Tumor Prediction: A Comparative Study Of Machine Learning Algorithms  \nMuhammad Usman Bhatti  \n1*  \n,  \nAli Saeed  \n1  \n,  \nMuhammad Farhat Ullah  \n2,5  \n,  \nMuhammad Sauood  \n3,4  \n,  \nMuhammad Ashir  \n6  \n,  \nNaveed Hussain  \n1  \n1 Department of Software Engineering, FOIT, University of Central Punjab, Lahore, Pakistan;  \n2 School of Software, Dalian University of Technology, Dalian, Ganjingzi District, Liaoning Province, China; 3 Department of Computer Science, Bahria University, Lahore, Pakistan; 4 Faculty of Computer Science & Mathematics, Universiti Malaysia Terengganu; 5 Department of Software Engineering, FOIT, University of Lahore, Punjab, Pakistan; 6 Department of Software Engineering, University of South Asia Lahore, Punjab, Pakistan  \nAbstract  \nThe prediction of brain tumors using machine learning has become a pivotal area in medical diagnostics, offering the potential for enhanced early detection and treatment planning. This study evaluates the performance of various machine learning models in predicting brain tumors from medical clinical data. The eight models that we used for comparison are: ZeroR, K-Nearest Neighbour (KNN), KStar, J48, Multilayer Perceptron (MLP), Support Vector Machine (SVM), AdaBoost and Naïve Bayes. Features were also selected through feature selection, the data normalized and was split into training, validation and test sets with much attention paid to pre-processing the data. Based on the performance of each model on the training data set, we used other factors including precision, accuracy, recall, and F1 score to determine the performance of each model. The results prove that Naïve Bayes is the most accurate with 53.8%, with fairly even-recall, precision, and F1 measure, making it the best classifier that accurately predicts brain tumor. Overall, it was observed that SVM had the lowest values of accuracy, along with precision and recall. This comparative assessment indicates that Naïve Bayes is the most accurate of the models examined in this study and provides further understanding of the feasibility of using different algorithms to improve the identification and categorization of brain tumors. This analysis should be complemented by additional studies to develop these models and to examine other data characteristics.  \n*Correspondence author email [address:](address: muhammad.usman1@ucp.edu.pk)[ muhammad.usman1@ucp.edu.pk](address: muhammad.usman1@ucp.edu.pk)[ ](address: muhammad.usman1@ucp.edu.pk)[DOI: 0009-0009-7230-6942](DOI: 0009-0009-7230-6942)  \nThis work is licensed under a Creative Commons Attribution 3.0 License.  \nVFAST Transactions on Software Engineering Volume 12, Issue 4, 2024  \n1 Introduction  \nThe brain is an incredibly complex organ made up of billions of cells working together to control various functions in the human body. Brain tumors occur when cells grow uncontrollably, which can interfere with normal brain activities and harm healthy cells [1, 2] . Gliomas, a common type of brain tumor, are classified into four grades: Grades I and II are considered low grade (LG), while Grades III and IV are classified as high grade (HG) [3] . Despite significant progress in treatments like surgery, chemotherapy, and radiotherapy, malignant brain tumors are still difficult to treat. Reports show that brain tumors are the fifth leading cause of death among women aged 20 to 39 . Moreover, the average survival rate for individuals with primary brain tumors is 75.2 percent, emphasizing the critical need for early detection [4] . Due to a diverse nature of the features that maybe observed in the case of brain tumors, predicting them is a challenging task in diagnostics. Due to the complexity of the problem of tumor ","cbCainDRmLuxy5e5","https://ap.wps.com/l/cbCainDRmLuxy5e5","pdf",240577,1,11,"English","en",105,"# Introduction\n## Brain tumor background and diagnostic challenges\n## Machine learning motivation and scope\n## Model set and evaluation approach","[{\"question\":\"Which machine learning models are compared for brain tumor prediction?\",\"answer\":\"The study compares ZeroR, K-Nearest Neighbour (KNN), KStar, J48, Multilayer Perceptron (MLP), Support Vector Machine (SVM), AdaBoost, and Naïve Bayes.\"},{\"question\":\"How is the data prepared before training the models?\",\"answer\":\"Features are selected, the data are normalized, and the dataset is split into training, validation, and test sets with emphasis on preprocessing.\"},{\"question\":\"Which model performs best and how is it evaluated?\",\"answer\":\"Naïve Bayes is reported as the most accurate, achieving 53.8% accuracy, with fairly even precision, recall, and F1 score. Performance is evaluated using precision, accuracy, recall, and F1.\"}]","Optimizing Brain Tumor Prediction - A Comparative Study Of Machine Learning Algorithms | PDF",1785820600,28,{"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},"optimizing-brain-tumor-prediction-a-comparative-study-of-machine-learning-algorithms","",{"@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/optimizing-brain-tumor-prediction-a-comparative-study-of-machine-learning-algorithms/124125/",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","2026-08-04",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},"Which machine learning models are compared for brain tumor prediction?","Question",{"text":75,"@type":76},"The study compares ZeroR, K-Nearest Neighbour (KNN), KStar, J48, Multilayer Perceptron (MLP), Support Vector Machine (SVM), AdaBoost, and Naïve Bayes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the data prepared before training the models?",{"text":80,"@type":76},"Features are selected, the data are normalized, and the dataset is split into training, validation, and test sets with emphasis on preprocessing.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best and how is it evaluated?",{"text":84,"@type":76},"Naïve Bayes is reported as the most accurate, achieving 53.8% accuracy, with fairly even precision, recall, and F1 score. 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