[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127214-en":3,"doc-seo-127214-105":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},127214,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Detection of Prostate Cancer Using Machine Learning Techniques - An Exploratory Study","Prostate cancer is among the most common cancers in men, while current testing approaches remain challenging because overdiagnosis and overtreatment can cause significant side effects without clearly reducing mortality. Predictive tools are therefore critical for supporting clinical diagnosis. This study evaluates multiple machine learning techniques to improve sensitivity and specificity for detecting clinically significant disease using risk factors such as PSA, DRE, age, race/ethnicity, and family history. Results show Logistic Regression achieving the best overall performance, followed by Random Forest, SVM, and XG Boost.","Association 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 are aggressive have the ability to metastasize leading to higher death rates since predicting and treating metastatic cancer is complex and may not be effective (Wilbur, 2008) . Therefore, it is necessary to detect prostate cancer while it is still confined to the prostate gland. PCa can be diagnosed through various screening methods where DRE and PSA tests are the most widely used, easy to administer, and relatively low-cost screening tools for early prediction (Wilbur, 2008) . However, prostate biopsy remains the reference standard for PCa detection. Though biopsy is essential to confirm the presence of cancer, it also associated with different health complications such as infection, rectal bleeding, numbness, pain, and unnecessary financial expenses (Liu et al., 2019). Therefore, it is essential to minimize avoidable repeated biopsies by making screening tests efficient enough that they only lead to true positive prostate biopsy results. Many predictive tools of PCa are present in the literature th","cbCaisJvWsmQvLca","https://ap.wps.com/l/cbCaisJvWsmQvLca","pdf",409568,1,6,"English","en",105,"# Abstract\n# Introduction\n# Literature Review","[{\"question\":\"Why is detecting clinically significant prostate cancer important?\",\"answer\":\"Overdiagnosis and overtreatment can lead to adverse side effects with little impact on preventing death. Accurate predictive tools help clinicians focus on clinically significant cases.\"},{\"question\":\"Which risk factors are used in the predictive modeling?\",\"answer\":\"The study considers PSA and digital rectal examination (DRE), along with age, race/ethnicity, and family history.\"},{\"question\":\"Which machine learning model performed best in the results?\",\"answer\":\"Logistic Regression outperformed all evaluated models, followed by Random Forest, SVM, and XG Boost.\"}]","Detection of Prostate Cancer Using Machine Learning Techniques - An Exploratory Study | PDF",1785937571,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"detection-of-prostate-cancer-using-machine-learning-techniques-an-exploratory-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/detection-of-prostate-cancer-using-machine-learning-techniques-an-exploratory-study/127214/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is detecting clinically significant prostate cancer important?","Question",{"text":75,"@type":76},"Overdiagnosis and overtreatment can lead to adverse side effects with little impact on preventing death. Accurate predictive tools help clinicians focus on clinically significant cases.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which risk factors are used in the predictive modeling?",{"text":80,"@type":76},"The study considers PSA and digital rectal examination (DRE), along with age, race/ethnicity, and family history.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best in the results?",{"text":84,"@type":76},"Logistic Regression outperformed all evaluated models, followed by Random Forest, SVM, and XG Boost.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]