[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123172-en":3,"doc-seo-123172-105":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},123172,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Decision-making in clinical diagnostic for brain tumor detection based on advanced machine learning algorithm","Brain tumors are benign or malignant abnormal growths that disrupt brain function and lead to symptoms such as headaches, seizures, and cognitive decline. Reliable predictive models are essential for diagnosis and prognosis. This study builds brain tumor prediction models using machine learning enhanced by an optimizer, Escaping Bird Search Optimization. Ada Boost, Gaussian Process, and Support Vector classifiers are evaluated, with the optimized enhanced SVC (SVEB) achieving the highest accuracy across models. Results highlight the value of optimization for accurate neuro-oncology prediction.","Int. J. Simul. Multidisci. Des. Optim. 16, 1 (2025)© T. Huang et al., Published by EDP Sciences, 2025 [https://doi.org/10.1051/smdo/2024021](https://doi.org/10.1051/smdo/2024021)  \nAvailable online at:  \n[https://www.ijsmdo.org](https://www.ijsmdo.org)  \n| RESEARCH ARTICLE   |  |\n| --- | --- |\n\nDecision-making in clinical diagnostic for brain tumor detection based on advanced machine learning algorithm  \n*  \nTangsen Huang, Xiangdong Yin , and Ensong Jiang  \nSchool of Information Engineering, Hunan University of Science and Engineering, Yongzhou 425199, Hunan, China  \nReceived: 27 June 2024 / Accepted: 17 September 2024  \nAbstract. Brain tumors, abnormal growths in the brain or spinal canal, can be benign or malignant, causing symptoms like headaches, seizures, and cognitive decline by disrupting brain function. Therefore, developing reliable predictive models for diagnosis and prognosis is crucial. In this paper, the prediction of brain tumors is made using machine learning models enhanced by an optimizer, namely Escaping Bird Search Optimization.  \nOptimized models incorporate Ada Boost Classifier (ADEB), Gaussian Process Classifier (GPEB), and Support Vector Classifier (SVC) which, after being tested on a few databases, were named ADEB, SVEB, and GPEB, respectively, and their predictive power was assessed. The best single model performance overall on all databases is the SVC with an average accuracy of 0.981, while among enhanced models, the optimized model, called SVEB, using SVC, attained the highest accuracy for all models and reached as high as 0.990 . These findings underscore the role of optimization techniques and demonstrate the effectiveness of machine learning in predicting brain cancers. The improved performance of the enhanced SVC model, SVEB, suggests it could offer a reliable approach for accurate brain tumor prediction. Enhanced patient outcomes and early diagnosis could be an implication of this in the field of neuro-oncology.  \nKeywords: Brain tumor classification / machine learning / Ada boost classifier / Gaussian process classifier / support vector classifier  \n1 Introduction  \nThe human brain is a complex interconnection of neurons and synapses. It acts as the central nervous system to coordinate various physiological and cognitive tasks essential in life. Like other major organs, it can be subject to various degenerative diseases. One particularly serious problem pertains to that of tumors of the brain. Brain tumors are a heterogeneous group of neoplastic entities, diverging in their clinical relevance. They are characterized by the abnormal proliferation of cells inside the brain or central spinal canal. These tumors represent a heterogeneous group, including benign growths and malignant malignancies. They may arise from many types of neural cells or from the metastasis of cancerous cells that have developed in other areas of the body [1] . The diagnosis and management strategy for brain tumors appropriately requires the etiological factors, clinical symptoms, and therapeutic techniques involved task that calls for a complex interplay of neurological structures and functions. Such insight not only helps medical professionals make decisions about diagnosis and treatment but also has  \n* e-mail: [yinxiangdong@huse.edu.cn](yinxiangdong@huse.edu.cn)  \nsignificant ramifications for improving patient outcomesand the quality of life when dealing with this powerful pathogenic entity [2] .  \n1.1 Causes of brain tumors  \nThere are some known risk factors for brain tumors, although the exact cause remains largely unknown. These include exposure to ionizing radiation, genetic predisposition, impairment of the immune system, and some genetic disorders like neurofibromatosis and Li-Fraumeni syndrome. Furthermore, several reports indicate that exposure to electromagnetic fields and certain environmental chemicals may be associated with brain malignancies; however, further investigation is still required to verify the results [3,4","cbCaiqbpRw0N8XVC","https://ap.wps.com/l/cbCaiqbpRw0N8XVC","pdf",4231082,1,17,"English","en",105,"# Introduction\n## Causes of brain tumors\n## Signs and symptoms\n## Diagnostic techniques\n## Treatment options","[{\"question\":\"Why is decision-making important for clinical brain tumor diagnosis?\",\"answer\":\"Brain tumors can be benign or malignant and cause neurologic symptoms by disrupting brain function, so dependable diagnostic and prognostic prediction supports better clinical decisions and outcomes.\"},{\"question\":\"Which optimization method and classifiers are used in the study?\",\"answer\":\"The models use Escaping Bird Search Optimization to enhance machine learning classifiers, including Ada Boost Classifier (ADEB), Gaussian Process Classifier (GPEB), and Support Vector Classifier (SVC), evaluated as ADEB, GPEB, and SVEB.\"},{\"question\":\"What model achieved the best predictive performance?\",\"answer\":\"Across all databases, the best overall single-model performance is reported for SVC with an average accuracy around 0.981, while the optimized enhanced SVC model SVEB reaches up to about 0.990 accuracy.\"}]","Decision-making in clinical diagnostic for brain tumor detection based on advanced machine learning algorithm | 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is decision-making important for clinical brain tumor diagnosis?","Question",{"text":75,"@type":76},"Brain tumors can be benign or malignant and cause neurologic symptoms by disrupting brain function, so dependable diagnostic and prognostic prediction supports better clinical decisions and outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which optimization method and classifiers are used in the study?",{"text":80,"@type":76},"The models use Escaping Bird Search Optimization to enhance machine learning classifiers, including Ada Boost Classifier (ADEB), Gaussian Process Classifier (GPEB), and Support Vector Classifier (SVC), evaluated as ADEB, GPEB, and SVEB.",{"name":82,"@type":73,"acceptedAnswer":83},"What model achieved the best predictive performance?",{"text":84,"@type":76},"Across all databases, the best overall single-model performance is reported for SVC with an average accuracy around 0.981, while the optimized enhanced SVC model SVEB reaches up to about 0.990 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