[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125069-en":3,"doc-seo-125069-105":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":20,"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},125069,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Construction of a risk prediction model for postoperative deep vein thrombosis in colorectal cancer patients based on machine learning algorithms","Machine learning algorithms are developed and validated to predict the risk of postoperative lower-limb deep vein thrombosis (DVT) in colorectal cancer patients. A retrospective cohort of 429 patients (Jan 2021–Jan 2024) is used with data including demographics, laboratory tests, BMI, comorbidities, clinical staging, histology, surgical approach, and postoperative complications. Imbalanced data handling uses SMOTE, feature selection applies RF, XGBoost, and LASSO, and model performance is assessed with ROC-AUC and other classification metrics. Interpretability is enhanced via SHAP and LIME, showing XGBoost as best-performing.","TYPE Original Research PUBLISHED 27 November 2024 DOI 10.3389/fonc.2024.1499794  \nOPEN ACCESS  \nEDITED BY  \nMohsin Saleet Jafri,  \nGeorge Mason University, United States  \nREVIEWED BY  \nEric Munger,  \nUnited States Department of Veterans Affairs, United States  \nSoukaina Amniouel,  \nNational Center for Advancing Translational Sciences (NIH), United States  \n*CORRESPONDENCE  \nYifan Jiang  \n[jyf016023@163.com](jyf016023@163.com)  \nRECEIVED 21 September 2024  \nACCEPTED 05 November 2024  \nPUBLISHED 27 November 2024  \nCITATION  \nLiu X, Shu X, Zhou Y and Jiang Y (2024) Construction of a risk prediction model for postoperative deep vein thrombosis in colorectal cancer patients based on machine learning algorithms.  \nFront. Oncol. 14:1499794 .  \ndoi: 10.3389/fonc.2024.1499794  \nCOPYRIGHT  \n© 2024 Liu, Shu, Zhou and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nConstruction of a risk prediction model for postoperative deep vein thrombosis in colorectal cancer patients based on machine learning algorithms  \nXin Liu 1, Xingming Shu 1, Yejiang Zhou 2 and Yifan Jiang 2*  \n1 Department of Clinical Medicine, Southwest Medical University, Luzhou, China, 2 Department of Gastrointestinal Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China  \nBackground: Colorectal cancer is a prevalent malignancy of the digestive system, with an increasing incidence. Lower extremity deep vein thrombosis (DVT) is a frequent postoperative complication, occurring in up to 40% of cases.  \nObjective: This research aims to develop and validate a machine learning model (ML) to predict the risk of lower limb deep vein thrombosis in patients with colorectal cancer, facilitating preventive and therapeutic measures to enhance recovery and ensure safety.  \nMethods: In this retrospective cohort study, we collected data from 429 colorectal cancer patients from January 2021 to January 2024 . The medical records included age, blood test results, body mass index, underlying diseases, clinical staging, histological typing, surgical methods, and postoperative complications. We employed the Synthetic Minority Oversampling Technique to address imbalanced data and split the dataset into training and validation sets in a 7:3 ratio. Feature selection was performed using Random Forest (RF), XGBoost, and Least Absolute Shrinkage and Selection Operator algorithms (LASSO) . We then trained six machine learning models: Logistic Regression (LR), Naive Bayes (NB), Gaussian Process (GP), Random Forest, XGBoost, and Multilayer Perceptron (MLP) . The model ’s performance was evaluated using metrics such as area under the Receiver Operating Characteristic curve, accuracy, sensitivity, specificity, F1 score, and confusion matrix. Additionally, SHAP and LIME were used to enhance the interpretability of the results.  \nResults: The study combined Random Forest, XGBoost algorithms, and LASSO regression with univariate regression analysis to identify significant predictive factors, including age, preoperative prealbumin, preoperative albumin, preoperative hemoglobin, operation time, PIKVA2, CEA, and preoperative neutrophil count. The XGBoost model outperformed other ML algorithms, achieving an AUC of 0.996, an accuracy of 0.9636, a specificity of 0.9778, and an F1 score of 0 .9576. Moreover, the SHAP method identified age and preoperative prealbumin as the primary determinants influencing ML model predictions. Finally, the study employed LIME for more precise prediction and interpretation of individual predictions.  \nFrontiers in Oncology 01 [frontiersin.org](frontie","cbCaiqQOfqRxuLW4","https://ap.wps.com/l/cbCaiqQOfqRxuLW4","pdf",3975185,1,14,"English","en",105,"# Introduction\n# Methods\n## Data collection and preprocessing\n## Feature selection and model training\n## Evaluation and interpretability\n# Results\n# Conclusion","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To develop and validate a machine learning model that predicts the risk of lower-limb deep vein thrombosis after surgery in colorectal cancer patients, supporting preventive and therapeutic measures.\"},{\"question\":\"How was the model built and validated?\",\"answer\":\"The study used a retrospective cohort of 429 patients from 2021–2024, applied SMOTE for imbalanced data, selected features using RF, XGBoost, and LASSO, trained six ML models, and evaluated performance with metrics such as ROC-AUC, accuracy, sensitivity, specificity, F1 score, and a confusion matrix.\"},{\"question\":\"Which model performed best and what interpretability tools were used?\",\"answer\":\"XGBoost outperformed other algorithms, achieving an AUC of 0.996 and an accuracy of 0.9636. SHAP and LIME were used to improve interpretability by identifying key determinants and explaining individual predictions.\"}]","Construction of a risk prediction model for postoperative deep vein thrombosis in colorectal cancer patients based on machine learning algorithms | PDF",1785896457,35,{"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},"construction-of-a-risk-prediction-model-for-postoperative-deep-vein-thrombosis-in-colorectal-cancer-patients-based-on-machine-learning-algorithms","",{"@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/construction-of-a-risk-prediction-model-for-postoperative-deep-vein-thrombosis-in-colorectal-cancer-patients-based-on-machine-learning-algorithms/125069/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study?","Question",{"text":75,"@type":76},"To develop and validate a machine learning model that predicts the risk of lower-limb deep vein thrombosis after surgery in colorectal cancer patients, supporting preventive and therapeutic measures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the model built and validated?",{"text":80,"@type":76},"The study used a retrospective cohort of 429 patients from 2021–2024, applied SMOTE for imbalanced data, selected features using RF, XGBoost, and LASSO, trained six ML models, and evaluated performance with metrics such as ROC-AUC, accuracy, sensitivity, specificity, F1 score, and a confusion matrix.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and what interpretability tools were used?",{"text":84,"@type":76},"XGBoost outperformed other algorithms, achieving an AUC of 0.996 and an accuracy of 0.9636. 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