[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122660-en":3,"doc-seo-122660-105":29,"detail-sidebar-cat-0-en-105":94},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},122660,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Lifestyle and Occupational Risks Assessment of Bladder Cancer Using Machine Learning-Based Prediction Models","Bladder cancer is a major cause of morbidity and mortality and is influenced by environmental exposure and other risk factors such as inflammation. A population-based case–control study developed machine learning risk prediction models for bladder cancer using 692 cases and 692 healthy controls. Neural Network, Random Forest, Decision Tree, Naive Bayes, Gradient Boosting, and Logistic Regression were evaluated. Random Forest achieved the best performance (AUC 0.86, precision 79%). Key predictors included recurrent infection, bladder stone history, smoking/opium use, occupational exposures, chronic renal failure, and dietary factors.","Received: 12 February 2023 Revised: 22 June 2023 Accepted: 25 June 2023  \nDOI: 10.1002/cnr2.1860  \nO R IG INA L ARTI CLE  \nLifestyle and occupational risks assessment of bladder cancer using machine learning-based prediction models  \nNaser Shakhssalim 1 | Atefeh Talebi 2 | Mohammad-Taha Pahlevan-Fallahy 3 |  \nKasra Sotoodeh 3 | Hamid Alavimajd 4 | Nasrin Borumandnia 1  | Maryam Taheri 1   \n1Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran  \n2British Heart Foundation Cardiovascular Research Centre, University of Glasgow, Glasgow, UK  \n3Students' Scientific Research Center, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran  \n4Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran  \nCorrespondence  \nNasrin Borumandnia and Maryam Taheri, Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.  \nEmail: [borumand.n@gmail.com](borumand.n@gmail.com) and [taheri233@yahoo.com](taheri233@yahoo.com)  \nAbstract  \nBackground: Bladder cancer, one of the most prevalent cancers globally, can be regarded as considerable morbidity and mortality for patients. The bladder is an organ that comes in constant exposure to the environment and other risk factors such as inflammation.  \nAims: In the current study, we used machine learning (ML) methods and developed risk prediction models for bladder cancer.  \nMethods: This population-based case–control study is focused on 692 cases of bladder cancer and 692 healthy people. The ML, including Neural Network (NN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Gradient Boosting (GB), and Logistic Regression (LR), were applied, and the model performance was evaluated.  \nResults: The RF (AUC = .86, precision = 79%) had the best performance, and the RT (AUC = . 78, precision = 73%) was in the next rank. Based on variable importance analysis in RF, recurrent infection, bladder stone history, neurogenic bladder, smoking and opium use, chronic renal failure, spinal cord paralysis, analgesic, family history of bladder cancer, diabetic mellitus, low dietary intake of fruit and vegetable, high dietary intake of ham, sausage, can and pickles were respectively the most important factors, which effect on the probability of bladder cancer.  \nConclusion: Machine learning approaches can predict the probability of bladder cancer according to medical history, occupational risk factors, and dietary and demographical characteristics.  \nKEYWOR DS  \nbladder cancer, machine learning, predictive models  \n1 | INTRODUCTION  \nCancer is one of the main causes of death and morbidity nowadays. According to the global cancer observatory, bladder cancer is the 10th most frequent cancer in the general population.1 Worldwide studies of cancer demonstrate that 1 out of every 100 men or 400 woman  \nexperience bladder cancer during their lifetime.2 Many risk factors contribute to bladder cancer, which can be categorized into genetic predisposition and exposure to external carcinogens. Evidence showed that many cases of bladder cancer could be attributed to external risk factors, namely smoking and tobacco, family history of smoking or tobacco use or workplace exposure to cigarette smoke,  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2023 The Authors. Cancer Reports published by Wiley Periodicals LLC.  \nCancer Reports. 2023;6:e1860 .  \n[https://doi.org/10.1002/cnr2.1860](https://doi.org/10.1002/cnr2.1860)  \n[wileyonlinelibrary.com/journal/cnr2](wileyonlinelibrary.com/journal/cnr2)  \n1 of 8  \n2 of 8  \nSHAKHSSALIM  \nET AL.  \nand past medical history such as a history of bladder stones, neurogenic bladder, recurrent urinary tract infections (UTI), family history of bladder cancer, and diabe","cbCaiuWNuZbplTrK","https://ap.wps.com/l/cbCaiuWNuZbplTrK","pdf",1552278,1,"English","en",105,"# Abstract\n## Background and Aims\n## Methods\n## Results\n## Conclusion\n# Introduction\n# Methods","[{\"question\":\"What was the purpose of this study on bladder cancer risk prediction?\",\"answer\":\"To build machine learning–based risk prediction models that estimate the probability of bladder cancer using medical history, occupational risk factors, dietary intake, and demographic characteristics.\"},{\"question\":\"How was the study designed and what dataset size was used?\",\"answer\":\"It was a population-based case–control study including 692 bladder cancer cases and 692 healthy controls.\"},{\"question\":\"Which machine learning model performed best and what were its key evaluation results?\",\"answer\":\"Random Forest performed best with AUC 0.86 and precision 79%.\"},{\"question\":\"What variables were identified as most important for bladder cancer probability?\",\"answer\":\"Variable importance in Random Forest highlighted recurrent infection, bladder stone history, neurogenic bladder, smoking and opium use, chronic renal failure, spinal cord paralysis, analgesic use, family history, diabetes mellitus, and specific dietary factors.\"}]","Lifestyle and Occupational Risks Assessment of Bladder Cancer Using Machine Learning-Based Prediction Models | 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was the purpose of this study on bladder cancer risk prediction?","Question",{"text":74,"@type":75},"To build machine learning–based risk prediction models that estimate the probability of bladder cancer using medical history, occupational risk factors, dietary intake, and demographic characteristics.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How was the study designed and what dataset size was used?",{"text":79,"@type":75},"It was a population-based case–control study including 692 bladder cancer cases and 692 healthy controls.",{"name":81,"@type":72,"acceptedAnswer":82},"Which machine learning model performed best and what were its key evaluation results?",{"text":83,"@type":75},"Random Forest performed best with AUC 0.86 and precision 79%.",{"name":85,"@type":72,"acceptedAnswer":86},"What variables were identified as most important for bladder cancer probability?",{"text":87,"@type":75},"Variable importance in Random Forest highlighted recurrent infection, bladder stone 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