[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125135-en":3,"doc-seo-125135-105":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},125135,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Intelligent Cervical Cancer Detection - Empowering Healthcare with Machine Learning Algorithms","Cervical cancer is a major global health problem, especially in underdeveloped regions where late diagnosis drives high mortality. This study applies machine learning models to predict cervical cancer risk using a UCI dataset with demographic and clinical attributes, including age, sexual history, smoking habits, and medical history. After preprocessing, multiple classifiers (logistic regression, SVM, random forest, decision tree, AdaBoost, and ANN) are trained and evaluated with precision, recall, and F1 score. Results show ANN achieves the highest accuracy (0.95), and correlation analysis identifies meaningful relationships among risk factors, supporting earlier detection and improved patient outcomes.","Intelligent cervical cancer detection: empowering healthcare with machine learning algorithms  \nUma Yadav1, Vipin D. Bondre2, Shweta V. Bondre1, Bhakti Thakre3, Poorva Agrawal4, Shruti Thakur5  \n1School of Computer Science and Engineering, Ramdeobaba University, Formerly Shri Ramdeobaba College of Engineering and  \nManagement, Nagpur, India  \n2Department of Electronics and Telecommunication Engineering, Yeshwantrao Chavan College of Engineering, Nagpur, India 3Department of Computer Science and Engineering (Cyber Security), St. Vincent Pallotti College of Engineering and Technology,  \nNagpur, India  \n4Symbiosis Institute of Technology, Nagpur Campus Symbiosis International (Deemed University), Nagpur, India 5Department of Computer Science and Engineering, G H Raisoni College of Engineering, Nagpur, India  \nArticle history:  \nReceived Apr 4, 2024 Revised Jul 17, 2024 Accepted Jul 26, 2024  \nKeywords:  \nArtificial neural networks Cervical cancer Logistic regression Machine learning Random forest  \nSupport vector machine  \nCorresponding Author:  \nCervical cancer remains a significant global health issue, particularly in underdeveloped nations, where it contributes to high mortality rates. Early detection is critical for improving treatment outcomes and survival rates. This study employs machine learning (ML) algorithms to predict cervical cancer risk using a dataset from the University of California at Irvine (UCI), which includes demographic and clinical attributes such as age, sexual history, smoking habits, and medical history. After applying data preprocessing techniques, several classification algorithms, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree, adaptive boosting (AdaBoost), and artificial neural networks (ANN), were trained and evaluated. The models were assessed using classification metrics such as precision, recall, and F1 score. Among the models, the ANN demonstrated the highest accuracy, achieving a score of 0.95. In addition, correlation analysis revealed significant relationships between various risk factors, providing insights into cervical cancer mechanisms and potential preventive measures. The study highlights the potential of ML in improving cervical cancer detection and patient outcomes, suggesting that advanced ML techniques can be valuable tools in healthcare research and clinical applications.  \nThis is an open access article under the CC BY-SA license.  \nUma Yadav  \nSchool of Computer Science and Engineering, Ramdeobaba University Formerly Shri Ramdeobaba College of Engineering and Management Nagpur 440013, India  \n[Email: uma.yadav12@gmail.com](Email: uma.yadav12@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nWorldwide, the health of women is at risk from the lethal illness known as cervical cancer, and it can be challenging to identify its early symptoms [1] . Cervical tumor, which has abundant negative things, is solitary of the most severe illnesses people might have. Women's ignorance of the value of early detection contributes to the higher mortality rate of uterine cancer. It grounds damage to the cervix's yawning tissues and can ultimately spread to further portions of the body, such as the lungs, vagina, and liver which can mark the condition more stimulating [2]. Brain tumour diagnosis, breast cancer recognition, cervical cancer recognition, physical activity recognition, COVID detection, thermal sensation recognition, and the evaluation of dementia patients' cognitive health all make use of machine learning (ML) and deep learning (DL) . It is more efficient  \nthan conventional diagnostic methods thanks to developments in the healthcare sector. Every year, 493,000 new cases of cervical cancer are reported in health reports issued by “Global cancer statistics”, 15% of which are female cancer patients. With an 83% mortality rate, this illness is primarily prevalent in underdeveloped nations. prominent in African nations, such as Ugan","cbCaisOiwSseLyeS","https://ap.wps.com/l/cbCaisOiwSseLyeS","pdf",513270,1,9,"English","en",105,"# Article Info\n## Abstract\n## Keywords\n## Introduction\n## Methods and Evaluation","[{\"question\":\"What dataset is used to predict cervical cancer risk?\",\"answer\":\"The study uses a dataset from the University of California at Irvine (UCI) containing demographic and clinical attributes such as age, sexual history, smoking habits, and medical history.\"},{\"question\":\"Which machine learning models are trained and compared?\",\"answer\":\"Logistic regression, support vector machine, random forest, decision tree, adaptive boosting (AdaBoost), and artificial neural networks (ANN) are trained and evaluated.\"},{\"question\":\"What model performs best and what is the reported accuracy?\",\"answer\":\"The ANN model shows the highest accuracy, achieving a score of 0.95 based on the study’s evaluation metrics.\"}]","Intelligent Cervical Cancer Detection - 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