[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123955-en":3,"doc-seo-123955-105":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},123955,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",7,"Healthcare","Predicting vulnerability for brain tumor - Data-driven approach utilizing machine learning","Brain tumors, benign or malignant, pose a complex healthcare challenge influenced by genetic, environmental, and sometimes unknown factors, making timely, personalized risk assessment difficult. This study predicts brain-tumor vulnerability by leveraging health risk factors and symptoms with machine learning. Models including support vector machine, multi-layer perceptron, and logistic regression are trained and evaluated using accuracy, precision, recall, and F1-score, with results indicating SVM achieves the best performance. The findings are further packaged into a mobile application to support early detection and decision-making for improved patient outcomes and quality of life.","Predicting vulnerability for brain tumor: data-driven approach utilizing machine learning  \nYutika Amelia Effendi1, Amila Sofiah1, Niko Azhari Hidayat2, Awol Seid Ebrie3, Zainy Hamzah4  \n1Robotics and Artificial Intelligence Engineering, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya, Indonesia  \n2Industrial Engineering, Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya, Indonesia 3Department of Industrial and Data Engineering, College of Engineering, Pukyong National University, Busan, South Korea 4Faculty of Medicine and Health, University of Muhammadiyah Jakarta, Jakarta, Indonesia  \nArticle history:  \nReceived Jan 26, 2024 Revised Apr 16, 2024 Accepted May 6, 2024  \nKeywords:  \nBrain tumor Early detection Health risk  \nHealthcare Machine learning Technology Vulnerability  \nCorresponding Author:  \nBrain tumors, whether benign or malignant, present a complex and multifaceted challenge in healthcare, affecting individuals across various age groups. Predicting the vulnerability of brain tumors using health risk factors and symptoms is crucial, yet there have been limited research studies, particularly those integrating artificial intelligence (AI) technology. This research explores machine learning models such as support vector machines (SVMs), multi-layer perceptrons (MLPs), and logistic regression (LR) for the early detection of brain tumors. Evaluation metrics, including accuracy, precision, recall, and F1-score, are employed to assess model performance. The results indicate that the SVM outperforms other models, providing a robust foundation for predictive accuracy. To enhance accessibility and usability, the research also integrates these models into a mobile application predictor. The application is beneficial for assisting individuals in early detection by identifying potential risk factors and symptoms that may lead toa brain tumor. In conclusion, the integration of machine learning through a mobile application represents a transformative approach to personalized healthcare. By empowering individuals with cutting-edge technology, this research strives to enhance early detection and decision-making regarding potential brain tumor risks and symptoms, ultimately contributing to improved patient outcomes and quality of life.  \nThis is an open access article under the CC BY-SA license.  \nYutika Amelia Effendi  \nRobotics and Artificial Intelligence Engineering, Faculty of Advanced Technology and Multidiscipline Universitas Airlangga  \nSurabaya, Indonesia  \n[Email: yutika.effendi@ftmm.unair.ac.id](Email: yutika.effendi@ftmm.unair.ac.id)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe development of brain tumors is a multifaceted process influenced by genetic, environmental, and often unknown factors [1] . Various elements, such as genetic predisposition, exposure to environmental toxins, and a history of radiation therapy to the head, contribute to the complex etiology of these tumors [2] . Notably, brain tumors can affect individuals across age groups, from the youngest to adults, with certain genetic conditions and syndromes increasing the risk in children [3] .  \nBrain tumors, whether benign or malignant, pose a significant threat to both neurological and overall health [4] . The complexity of these tumors complicates the accurate prediction of patient vulnerability, impeding timely intervention and personalized treatment strategies. This is where machine learning, a subset of artificial intelligence (AI) technique, emerges as a transformative solution, using algorithms to analyze  \nextensive datasets, unveiling patterns and correlations not easily discernible by human observation [5]−[7] . Predicting the vulnerability of brain tumors using health risk factors and symptoms is crucial, yet there have been limited research studies, particularly those integrating AI technology. Some studies have focused on predicting survival in patients with brain tumors ","cbCaioVQfwREXKDu","https://ap.wps.com/l/cbCaioVQfwREXKDu","pdf",513677,1,11,"English","en",105,"# Introduction\n## Background and clinical challenge\n## Role of machine learning in prediction\n## Project goal and mobile application integration","[{\"question\":\"What problem does the study address regarding brain tumors?\",\"answer\":\"The study targets the difficulty of accurately predicting brain-tumor vulnerability in order to enable earlier intervention and more personalized treatment strategies.\"},{\"question\":\"Which machine learning models are used for vulnerability prediction?\",\"answer\":\"Support vector machine (SVM), multi-layer perceptron (MLP), and logistic regression (LR) are used to model vulnerability based on risk factors and symptoms.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Performance is assessed using accuracy, precision, recall, and F1-score to measure how well each model predicts vulnerability.\"}]","Predicting vulnerability for brain tumor - 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