[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125260-en":3,"doc-seo-125260-105":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":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},125260,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Breast Cancer Prediction Using Hybrid Machine Learning and Nature-Inspired Adaptive Optimization Algorithms - Abstract","An early stage of breast cancer occurrence does not have pain as a symptom, making timely and accurate prediction essential for better outcomes. This research investigates hybridizing four intelligent machine learning models—KNN, Naïve Bayes, SVM, and ANN—with three nature-inspired adaptive optimization approaches: APSO, AGA, and AVFO. Twelve hybrid predictive models are evaluated on UCI breast cancer datasets (WBC, WDBC, WPBC) and validated with and without feature selection using accuracy, F-measure, and G-mean. Results indicate AVFO-SVM, AVFO-ANN, and APSO-ANN as top performers, with ANN and SVM + AVFO showing robustness across all datasets.","Breast Cancer Prediction Using Hybrid Machine Learning and NatureInspired Adaptive Optimization Algorithms  \nTamilselvi Madeswaran1*, Aruna Kumar Kavuru2, Padma Theagarajan3, Nasser Al Hadrami4, Ohm Rambabu5, Maya Al Foori6  \n1*Lecturer, IT department, University of Technology and Applied Sciences, Nizwa, Sultanate of Oman [Email:-tamilselvi.madeswaran@nct.edu.om](Email:-tamilselvi.madeswaran@nct.edu.om)  \n2Lecturer, IT department,University of Technology and Applied Sciences, Nizwa, Sultanate of Oman [Email:-arun.kavuru@nct.edu.om](Email:-arun.kavuru@nct.edu.om)  \n3Professor, Department of Computer Applications, Sona College of Technology Salem, Tamilnadu, India [Email:-](Email:-padmat@sonatech.ac.in)[padmat@sonatech.ac.in](Email:-padmat@sonatech.ac.in)  \n4Senior Lecturer, IT department,University of Technology and Applied Sciences, Nizwa, Sultanate of Oman [Email:-nasser.almur@nct.edu.om](Email:-nasser.almur@nct.edu.om)  \n5Technical Support, ETC department,University of Technology and Applied Sciences, Nizwa, Sultanate of Oman [Email:-ram.babu@nct.edu.om](Email:-ram.babu@nct.edu.om)  \n6Oncology Nursing and Clinical Educator,National Oncology Center, Royal Hospital, Sultanate of Oman [Email:-lianobaby@gmail.com](Email:-lianobaby@gmail.com)  \n*Corresponding Author:-Tamilselvi Madeswaran  \n*Lecturer, IT department, University of Technology and Applied Sciences, Nizwa, Sultanate of Oman [Email:-tamilselvi.madeswaran@nct.edu.om](Email:-tamilselvi.madeswaran@nct.edu.om)  \nKEYWORDS:  \nBreast cancer  \nprediction, Intelligent predictors, Hybrid modelling strategies, Adaptive optimization approaches, Natureinspired optimization, UCI data repository.  \nABSTRACT  \nAn early stage of breast cancer occurrence does not have pain as a symptom. This asymptomatic nature of cancer necessitates the need of timely and accurate prediction using other potential indicators. Breast cancers are highly curable if predicted and diagnosed at the earliest. This research explores the capacity of hybridizing intelligent learning models with nature-inspired optimization algorithms for breast cancer prediction. This integration becomes pivotal to optimize internal parameters of the dataset while aiming for augmented accuracy of the proposed predictive models. Each of the four intelligent machine learning models including K-Nearest Neighbor (KNN), Naïve Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN) are hybridized with all the three adaptive optimization approaches including Adaptive Particle Swarm Optimization (APSO), Adaptive Genetic Algorithm (AGA) and Adaptive Venus Flytrap Optimization (AVFO) . Thus twelve hybridized predictive models were derived namely APSO-KNN, AGA-KNN, AVFO-KNN, APSO-NB, AGANB, AVFO-NB, APSO-SVM, AGA-SVM, AVFO-SVM, APSO-ANN, AGA-ANN and AVFO-ANN. These hybridized models were investigated through UCI data repository’s breast cancer dataset namely WBC, WDBC and WPBC. The experimental results were validated against the respective learning models with and without feature selection. Based on the performance measures such as accuracy, F-measure and G-mean, the outperformed learning models with WBC, WPBC and WDBC datasets areAVFO-SVM, AVFO-ANN and APSO-ANN respectively. Conclusively ANN and SVM machine learning algorithms fused with AVFO for feature selection are robust for all the three  \ndatasets. The derived hybrid intelligent models trained with tuned datasets optimize the  prediction ability of existing breast cancer prediction models.   \n1. INTRODUCTION  \nBreast cancer (BC) is a most common lethal cancer worldwide and is the primary cause of cancer deaths among women, strangely affecting low- and middle-  \nincome countries. There are more than 2.3 million cases of breast cancer that occur each year and in 95% of countries breast cancer is the leading cause of female cancer deaths. According to world health organization  \n(WHO), data estimates indicates that a substantial upsurge in cancer mortality to nea","cbCaiiWDKIHtdlxK","https://ap.wps.com/l/cbCaiiWDKIHtdlxK","pdf",783840,1,20,"English","en",105,"# Abstract\n## Keywords\n# 1. Introduction\n## Background and problem motivation\n## Need for intelligent prediction systems","[{\"question\":\"Why is early breast cancer prediction important even when symptoms are absent?\",\"answer\":\"Early-stage breast cancer often has no pain or noticeable symptoms. Timely and accurate prediction using other indicators supports earlier diagnosis and higher cure probability.\"},{\"question\":\"Which hybrid models were developed in the research?\",\"answer\":\"Four learning models (KNN, Naïve Bayes, SVM, ANN) were hybridized with three adaptive optimization approaches (APSO, AGA, AVFO), producing twelve models such as APSO-KNN and AVFO-SVM.\"},{\"question\":\"Which datasets and evaluation measures were used to validate performance?\",\"answer\":\"Models were tested on UCI datasets WBC, WDBC, and WPBC. Performance was assessed using accuracy, F-measure, and G-mean, comparing results with and without feature selection.\"}]","Breast Cancer Prediction Using Hybrid Machine Learning and Nature-Inspired Adaptive Optimization Algorithms - Abstract | PDF",1785897764,50,{"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},"breast-cancer-prediction-using-hybrid-machine-learning-and-nature-inspired-adaptive-optimization-algorithms-abstract","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/breast-cancer-prediction-using-hybrid-machine-learning-and-nature-inspired-adaptive-optimization-algorithms-abstract/125260/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is early breast cancer prediction important even when symptoms are absent?","Question",{"text":75,"@type":76},"Early-stage breast cancer often has no pain or noticeable symptoms. Timely and accurate prediction using other indicators supports earlier diagnosis and higher cure probability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which hybrid models were developed in the research?",{"text":80,"@type":76},"Four learning models (KNN, Naïve Bayes, SVM, ANN) were hybridized with three adaptive optimization approaches (APSO, AGA, AVFO), producing twelve models such as APSO-KNN and AVFO-SVM.",{"name":82,"@type":73,"acceptedAnswer":83},"Which datasets and evaluation measures were used to validate performance?",{"text":84,"@type":76},"Models were tested on UCI datasets WBC, WDBC, and WPBC. 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