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A retrospective dataset of 764 patients treated over 10 years is built with 44 predictor variables and one dependent variable (alive or dead). Models trained on an 80% training and 20% testing split are evaluated using sensitivity, accuracy, specificity, and ROC/AUC metrics. Results show comparable accuracy across algorithms, with CatBoost highest accuracy (0.813), random forest best precision (0.727), logistic regression highest recall (0.658), and random forest highest AUC (0.83), supporting timely, personalized interventions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-application-in-colon-cancer-treatment-outcome-prediction/353687/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-application-in-colon-cancer-treatment-outcome-prediction/353687.png","ImageObject",300,407,{"name":92,"@type":93},"Ezra","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What was the primary goal of the study?","Question",{"text":112,"@type":113},"To use machine learning techniques to build models that predict survival outcomes in colon cancer patients and compare predictive performance across algorithms.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the dataset constructed and how were patients split for modeling?",{"text":117,"@type":113},"A retrospective dataset of 764 colon cancer patients treated over 10 years was compiled with 44 predictors and one survival-status outcome. Data were split into 80% training and 20% testing sets.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning models were evaluated and what metrics were used?",{"text":121,"@type":113},"Random forest, logistic regression, XGBoost, gradient boosting, CatBoost, LightGBM, MLP, and 1D-CNN were evaluated. Performance was assessed using sensitivity, accuracy, specificity, and ROC curve with AUC.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},353687,1790820899,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1099514068035,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMachine learning application in colon cancer treatment outcome prediction  \nHadi Ghasemi1, Seyed Vahid Hosseini2, Abbas Rezaianzadeh2, Ali Reza Safarpour2, Hajar Khazraei2, Pooneh Mokarram3, Mozhdeh Zamani3, Seyed Ali Nabavizadeh1 & Alimohammad Bananzadeh2􀀍  \nColon cancer represents a significant global health burden, accounting for a substantial portion of cancer-related morbidity and mortality worldwide. Many studies have been conducted to predict survival outcomes; however, most of these analyses have been performed predominantly via basic statistical methods. The aim of this study was to perform machine learning techniques to build models for survival prediction in patients with colon cancer. A retrospective review of 764 colon cancer patients treated over a 10-year period facilitated the construction of a detailed dataset containing 44 predictor variables and one dependent variable, the survival status of the patients (alive or dead). The data were randomly split into 80% training and 20% testing sets. Prognostic features from the database were used to build machine learning algorithms, including random forest, logistic regression, XGBoost, gradient boosting, categorical boosting (CatBoost), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN) to predict progressive disease outcomes. Models were validated for sensitivity, accuracy and specificity, with predictive ability assessed by receiver operating characteristic (ROC) curve and area under the curve (AUC) calculations. In terms of model accuracy and precision, almost all algorithms produced similar outcomes; however, among the evaluated models, CatBoost achieved the highest accuracy of 0.813, and the random forest model demonstrated the best precision of 0.727, whereas the logistic regression model exhibited the highest recall of 0.658 on the test set. Our results revealed that the random forest algorithm exhibited the highest AUC of 0.83, demonstrating remarkable efficacy in achieving an optimal balance between sensitivity and specificity. In summary, this research highlights the potential of machine learning models to support personalized and timely interventions for colon cancer patients, ultimately aiming to improve patient care and outcomes.  \nKeywords Cancer, Colon cancer, Artificial intelligence, Machine learning algorithm, Artificial neural network  \nColon cancer represents a significant global health burden, accounting for a substantial portion of cancerrelated morbidity and mortality worldwide1–3. Despite advancements in diagnosis and treatment, accurately predicting survival outcomes for patients with colon cancer remains a complex and challenging endeavor. Moreover, the identification and validation of prognostic factors are crucial to improve treatment strategies. In recent years, the advent of Machine Learning (ML) techniques has revolutionized the field of oncology by offering sophisticated tools for prognostic modeling and risk stratification4,5. Traditionally, clinicians have used basic statistical analyses, such as descriptive statistics, chi-square tests, t tests, and regression models, which are often implemented through software programs such as SPSS, Microsoft Excel, and STATA, to analyze factors influencing colon cancer survival rates6,7. However, these conventional statistical methods are limited in their ability to develop predictive models and generate complex, integrative visualizations8. This limitation has led to the widespread adoption of various machine learning approaches, such as random forests, XGBoost, logistic regression, gradient boosting, CatBoost, LightGBM, MLP, and 1D-CNN in this field9–13. ML algorithms offer unique capabilities in capturing complex relationships within the data, thereby providing a more comprehensive perspective on survival prediction inpatients with colon","cbCaimRe71pORyKQ","https://ap.wps.com/l/cbCaimRe71pORyKQ","pdf",1409188,11,"English","# Background and Rationale\n# Study Design and Dataset\n## Models and Algorithms\n## Training/Testing Strategy\n# Evaluation Metrics\n# Results and Key Performance Findings\n# Implications for Personalized Care","[{\"question\":\"What was the primary goal of the study?\",\"answer\":\"To use machine learning techniques to build models that predict survival outcomes in colon cancer patients and compare predictive performance across algorithms.\"},{\"question\":\"How was the dataset constructed and how were patients split for modeling?\",\"answer\":\"A retrospective dataset of 764 colon cancer patients treated over 10 years was compiled with 44 predictors and one survival-status outcome. Data were split into 80% training and 20% testing sets.\"},{\"question\":\"Which machine learning models were evaluated and what metrics were used?\",\"answer\":\"Random forest, logistic regression, XGBoost, gradient boosting, CatBoost, LightGBM, MLP, and 1D-CNN were evaluated. Performance was assessed using sensitivity, accuracy, specificity, and ROC curve with AUC.\"}]","Machine learning application in colon cancer treatment outcome prediction | PDF",1790106741,28]