[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126751-en":3,"doc-seo-126751-105":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},126751,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Detection of Prostate Cancer Using Machine Learning Techniques: An Exploratory Study","Prostate Cancer (PCa) is a highly prevalent malignancy in men, and current testing faces major challenges due to overdiagnosis and overtreatment that can cause significant adverse side-effects without improving mortality outcomes. This research investigates how multiple machine learning techniques can enhance sensitivity and specificity for identifying clinically significant PCa. Models are built using key risk factors including PSA, DRE, age, race/ethnicity, and family history, with Logistic Regression performing best, followed by Random Forest, SVM, and XG Boost.","Dakota State University  \nBeadle Scholar  \n\n| Faculty Research & Publications | College of Business and Information Systems |\n| --- | --- |\n| 2023\u003Cbr>Detection of Prostate Cancer Using Machine Learning Techniques: An Exploratory Study\u003Cbr>Laxmi Manasa Gorugantu Omar El-Gayar\u003Cbr>Nevine Nawar\u003Cbr>Follow this and additional works at: [https://scholar.dsu.edu/bispapers](https://scholar.dsu.edu/bispapers) |  |\n\nRecommended Citation  \nGorugantu, L. M., El-Gayar, O., & Nawar, N. (2023) . Detection of Prostate Cancer Using Machine Learning Techniques: An Exploratory Study. MWAIS 2023 Proceedings. [https://aisel.aisnet.org/mwais2023/9](https://aisel.aisnet.org/mwais2023/9)  \nThis Conference Proceeding is brought to you for free and open access by the College of Business and Information Systems at Beadle Scholar. It has been accepted for inclusion in Faculty Research & Publications by an authorized administrator of Beadle Scholar. For more information, please contact [repository@dsu.edu](repository@dsu.edu).  \nAssociation for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| MWAIS 2023 Proceedings | Midwest (MWAIS) |\n| --- | --- |\n| 2023\u003Cbr>Detection of Prostate Cancer Using Machine Techniques: An Exploratory Study\u003Cbr>Laxmi Manasa Gorugantu Omar El-Gayar\u003Cbr>Nevine Nawar\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/mwais2023](https://aisel.aisnet.org/mwais2023) | Learning |\n\nThis material is brought to you by the Midwest (MWAIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in MWAIS 2023 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org).  \nDetection of Prostate Cancer Using Machine Learning Techniques: An Exploratory Study  \nLaxmi Manasa Gorugantu  \nDakota State University  \n[laxmi.gorugantu@trojans.dsu.edu](laxmi.gorugantu@trojans.dsu.edu)  \nOmar El-Gayar  \nDakota State University [omar.el-gayar@dsu.edu](omar.el-gayar@dsu.edu)  \nNevine Nawar  \nAlexandria University  \n[nevine.nawar@gmail.com](nevine.nawar@gmail.com)  \nABSTRACT  \nProstate Cancer (PCa) is one of the most frequent cancers worldwide and the most common cancer in males. Testing for PCa remains problematic. Evidence is mounting that overdiagnosis and over-treatment can result in adverse side-effects yet have little impact in preventing death from PCa. Consequently, the importance of predictive tools that help physicians in the diagnosis of the condition cannot be understated. Though there exist several predictive models for the detection of clinically significant PCa, these models mainly depend on logistic regression. The objective of this research is to investigate the potential of various machine learning techniques to improve the sensitivity and specificity of detecting clinically significant PCa. Risk factors considered include prostate-specific antigen (PSA), digital rectal examination (DRE), as well as age, race/ethnicity, and family history. According to the results, Logistic Regression has outperformed all the models followed by Random Forest, SVM and XG Boost.  \nKeywords  \nMachine Learning Techniques, Data Analysis, Data pre-processing, Prostate Cancer, Prognosis.  \nINTRODUCTION  \nProstate cancer is the cancer occurring in the prostate gland in men. It has been noted as the second most frequent cancer worldwide and the most common cancer in males around 84 countries, and is rapidly increasing in developed countries. The statistics show that every year around 240,000 US men are being diagnosed with prostate cancer and an average of 30,000 men succumb to prostate cancer every year ( Liu et al. , 2019). Prostate cancer can be a slow growing, low grade or insignificant cancer. Yet, significant numbers of positive cases and mortality rates indicate that PCa can also be devasting based on the aggressiveness of the disease (Liu et al., 2019). Significant prostate cancers that ar","cbCaisuRCIdwV3yP","https://ap.wps.com/l/cbCaisuRCIdwV3yP","pdf",425590,1,7,"English","en",105,"# Abstract\n# Introduction\n# Literature Review","[{\"question\":\"What problem does the study target in prostate cancer detection?\",\"answer\":\"The study addresses difficulties in prostate cancer testing, especially the trade-off between overdiagnosis/overtreatment and the need for tools that improve accurate identification of clinically significant cases.\"},{\"question\":\"Which risk factors are included as inputs for the predictive models?\",\"answer\":\"The models use PSA and digital rectal examination (DRE) plus demographic and clinical risk factors such as age, race/ethnicity, and family history.\"},{\"question\":\"Which machine learning method achieved the best performance in the study results?\",\"answer\":\"Logistic Regression outperformed the other evaluated models, followed by Random Forest, SVM, and XG Boost.\"}]","Detection of Prostate 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