[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117439-en":3,"doc-seo-117439-105":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},117439,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predicting Cirrhosis Patient Survival Utilizing Machine Learning Techniques - Thesis","This thesis evaluates multiple machine learning models to predict cirrhosis patient survival outcomes from clinical features, with the goal of comparing model performance and identifying the most useful predictors. Using the cirrhosis dataset from the UCI Machine Learning Repository and a synthesized dataset from Kaggle Playground, the study tests Logistic Regression, Random Forest, eXtreme Gradient Boosting, Support Vector Machine, and Multi-Layer Perceptron. Random Forest and XGBoost achieve the strongest results, with Random Forest reaching about 89% accuracy.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nPredicting Cirrhosis Patient Survival Utilizing Machine Learning Techniques  \nPermalink  \n[https://escholarship.org/uc/item/0d66m3m6](https://escholarship.org/uc/item/0d66m3m6)  \nAuthor  \nTu, Timothy Enoch  \nPublication Date  \n2025  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA Los Angeles  \nPredicting Cirrhosis Patient Survival Utilizing Machine Learning Techniques  \nA thesis submitted in partial satisfaction of the requirements for the degree Master of Applied Statistics and Data Science  \nby  \nTimothy Tu  \n© Copyright by Timothy Tu 2025  \nABSTRACT OF THE THESIS  \nPredicting Cirrhosis Patient  \nSurvival Utilizing  \nMachine Learning Techniques  \nby  \nTimothy Tu  \nMaster of Applied Statistics and Data Science  \nUniversity of California, Los Angeles, 2025  \nProfessor Ying Nian Wu, Chair  \nThis thesis presents a study utilizing different machine learning models for the prediction of patient survival outcomes based on clinical features associated with cirrhosis, aimed to compare performance and see which features are most useful for prediction. Utilizing the cirrhosis dataset from the UCI Machine Learning Repository and synthesized dataset from Kaggle Playground, this research analyzes the performance of Logistic Regression, Random Forest, eXtreme Gradient Boosting, Support Vector Machine, and Multi-Layer Perceptron. The Random Forest and XGBoost models performed the best, with the Random Forest model achieving an accuracy of 89% . Overall, this thesis demonstrated that machine learning models, especially ensemble methods, may serve as useful tools for clinical survival analysis.  \nThe thesis of Timothy Tu is approved.  \nNicolas Christou Hongquan Xu Ying Nian Wu, Committee Chair  \nUniversity of California, Los Angeles 2025  \nTABLE OF CONTENTS  \n1 Introduction ...................................... 1  \n2 Data Exploration ................................... 3  \n2.1 Explanatory Data Analysis (EDA) ....................... 5  \n2.1.1 Distribution of Status .......................... 6  \n2.1.2 Status vs Categorical Predictors ..................... 6  \n2.1.3 Status vs Numerical Predictors ..................... 11  \n2.1.4 Correlation Plot .............................. 13  \n3 Methodology ..................................... 15  \n3.1 Logistic Regression ................................ 16  \n3.1.1 Logistic Regression Model Performance ................. 17  \n3.2 Random Forest .................................. 17  \n3.2.1 Random Forest Model Performance ................... 18  \n3.3 XGBoost ...................................... 18  \n3.3.1 XGBoost Model Performance ...................... 19  \n3.4 SVM ........................................ 19  \n3.4.1 SVM Performance ............................ 20  \n3.5 MLP ........................................ 21  \n3.5.1 MLP Performance ............................ 22  \n3.6 Comparison of Models .............................. 23  \n4 Conclusion ....................................... 28  \nReferences ......................................... 30  \nLIST OF FIGURES  \n2.1 Distribution of Status ............................... 6  \n2.2 Distribution of Drug by Status .......................... 7  \n2.3 Distribution of Sex by Status .......................... 8  \n2.4 Distribution of Ascites by Status ........................ 9  \n2.5 Distribution of Edema by Status ......................... 10  \n2.6 Distribution of Stage by Status ......................... 11  \n2.7 Distribution of Age by Status .......................... 12  \n2.8 Distribution of Bilirubin by Status ....................... 13  \n2.9 Correlation Plot of Variables ........................... 14  \n3.1 ROC-AUC Curve for All Models ......................... 23  \n3.2 Feature Importance Plot of XGBoost Model .................. 25  \n3.3 Feature Importance Plot of Random Forest Mode","cbCaipyleUyo0XVb","https://ap.wps.com/l/cbCaipyleUyo0XVb","pdf",730131,1,38,"English","en",105,"# Introduction\n## Data Exploration\n## Methodology\n## Conclusion\n## References","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis focuses on predicting cirrhosis patient survival outcomes using clinical features, aiming to determine which predictors and models perform best.\"},{\"question\":\"Which machine learning models are evaluated?\",\"answer\":\"Logistic Regression, Random Forest, XGBoost, Support Vector Machine, and Multi-Layer Perceptron are analyzed using the available cirrhosis datasets.\"},{\"question\":\"Which models perform best and what accuracy is reported?\",\"answer\":\"Random Forest and XGBoost deliver the best performance; Random Forest reports about 89% accuracy.\"}]","Predicting Cirrhosis Patient Survival Utilizing Machine Learning Techniques - Thesis | PDF",1785675882,96,{"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},"predicting-cirrhosis-patient-survival-utilizing-machine-learning-techniques-thesis","",{"@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/predicting-cirrhosis-patient-survival-utilizing-machine-learning-techniques-thesis/117439/",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","2026-08-02",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},"What problem does the thesis address?","Question",{"text":75,"@type":76},"The thesis focuses on predicting cirrhosis patient survival outcomes using clinical features, aiming to determine which predictors and models perform best.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are evaluated?",{"text":80,"@type":76},"Logistic Regression, Random Forest, XGBoost, Support Vector Machine, and Multi-Layer Perceptron are analyzed using the available cirrhosis datasets.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models perform best and what accuracy is reported?",{"text":84,"@type":76},"Random Forest and XGBoost deliver the best performance; Random Forest reports about 89% accuracy.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]