[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125261-en":3,"doc-seo-125261-105":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},125261,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Model for Predicting Hepatocellular Carcinoma in Hepatitis C Patients - Research Summary","Chronic Hepatitis C affects an estimated 58 million people worldwide and substantially increases the risk of liver cancer, especially Hepatocellular Carcinoma (HCC). This study builds and evaluates an AdaBoost classification model using a Decision Tree base learner on a clinical dataset of patient indicators sourced from the UCI Machine Learning Repository. SMOTE balancing and ordinal encoding are applied, and hyperparameters are tuned manually. The model reaches 92.98% accuracy and AUROC 0.97, with class-wise differences in precision and recall, and supports future decision-system development for stage-wise HCC/HCV diagnosis.","Machine Learning Model for Predicting Hepatocellular Carcinoma in Hepatitis C Patients  \nArooj Fatima  \nDepartment of Science and Engineering Southampton Solent University Southampton, United Kingdom [aroojfatima497@outlook.com](aroojfatima497@outlook.com)  \nJarutas Andritsch  \nDepartment of Science and Engineering Southampton Solent University Southampton, United Kingdom [jarutas.andritsch@solent.ac.uk](jarutas.andritsch@solent.ac.uk)  \nAbstract— An estimated 58 million people suffer from chronic Hepatitis C virus around the world, while substantial evidence indicates that patients with Hepatitis C virus are at 17 times larger risk of developing Liver Cancer (Hepatocellular Carcinoma). Research has been carried out to predict hepatitis C and liver cancer at different stages in patients. In this research, we proposed the Classification model AdaBoost with Decision Tree as its base model to be trained and tested on patient dataset. The dataset contains records of clinical indicators and was acquired from the University of California Irvine Machine Learning Repository. The preparation of the dataset was done using balancing techniques i.e. SMOTE, it was encoded using Ordinal Encoding. The hyperparameters of AdaBoost model was tuned manually to find the most optimal combination. AdaBoost Classification Model achieved a 92.98% accuracy, and the AUROC of 0.97. The precision and recall differ for each class,“healthy” individuals were classified with a precision of 98% and a recall of 99% while patients with“Cirrhosis”(irreversible scarring of liver due to a tumor) were classified with 86% precision and 67% recall. The research concluded that machine learning has efficient applications in predicting diseases. Moreover, the clinical indicators mentioned in previous studies have proven to be vital in the prediction of HCC (liver cancer) however it is advised that, in future a larger dataset may be acquired to overcome any potential biases in the predictions. The current program successfully distinguishes between patients at different stages of HCC and HCV and can be further adapted to build decision systems to aid diagnosis.  \nKeywords—Hepatitis C, Hepatocellular Carcinoma, Machine Learning, SMOTE, Classification Models, AdaBoost  \nI. INTRODUCTION  \nHepatitis C is a virus (HCV), which causes the liver to be inflamed and infected. The World Health Organization [1] reported that 58 million people around the world have chronic Hepatitis C, and in 2019 an approximate 290,000 mortalities from Hepatitis C had also been diagnosed with Cirrhosis and Hepatocellular Carcinoma (HCC), the most common type of primary liver cancer. “Hepatocellular carcinoma risk increases to 17-folds in Hepatitis C Virus (HCV) infected patients compared to HCV-negative patients” [2] . Reference [3] found overwhelming epidemiological evidence indicating that patients with Hepatitis C virus are at a larger risk of developing Hepatocellular carcinoma (HCC) . Most of those suffering from HCV belong to developing countries, around 2-5% of general population in the subcontinent of India and the Middle East is chronically infected from HCV [4] . While a staggering 80% of those diagnosed with HCC reside in lowincome regions such as South-eastern Asia and sub-Saharan Africa. HCC has the shortest survival time among all types of cancers, and a prognosis is especially poorer in low-income countries, accredited to 2 main factors, a severe lack of  \nappropriate resources and diagnosed when the tumour has advanced to its final stages [5] .  \nFurther research suggests that an early diagnosis of Hepatocellular carcinoma can in-fact increase the 5-year survival rate to more than 70%, while 60% diagnoses are carried out after the metastasis has occurred, resulting in the 5-year survival rate to decrease to less than 16%[6] . This timely diagnosis is especially a challenge in low-income regions such as several countries in sub-Saharan Africa, where the ratio between doctors and patien","cbCaipMn9q5Wc60o","https://ap.wps.com/l/cbCaipMn9q5Wc60o","pdf",515084,1,6,"English","en",105,"# Introduction\n## Hepatitis C and HCC risk\n## Need for early diagnosis\n# Background\n## Disease progression and stages\n## Clinical diagnosis methods\n# Research focus","[{\"question\":\"What dataset and preprocessing steps are used to train the AdaBoost model?\",\"answer\":\"The study uses patient records with clinical indicators from the UCI Machine Learning Repository. It applies SMOTE for class balancing and uses ordinal encoding before training.\"},{\"question\":\"What performance metrics does the AdaBoost classification model achieve?\",\"answer\":\"AdaBoost achieves 92.98% accuracy with an AUROC of 0.97. Precision and recall vary by class, with higher performance for healthy individuals than for cirrhosis patients.\"},{\"question\":\"Why is early diagnosis of HCC in Hepatitis C patients important?\",\"answer\":\"Early diagnosis can raise the 5-year survival rate to over 70%, while diagnoses after metastasis reduce survival to below 16%. The work highlights limited resources in low-income regions as a key challenge.\"}]","Machine Learning Model for Predicting Hepatocellular Carcinoma in Hepatitis C Patients - Research Summary | PDF",1785897776,15,{"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},"machine-learning-model-for-predicting-hepatocellular-carcinoma-in-hepatitis-c-patients-research-summary","",{"@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/machine-learning-model-for-predicting-hepatocellular-carcinoma-in-hepatitis-c-patients-research-summary/125261/",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 dataset and preprocessing steps are used to train the AdaBoost model?","Question",{"text":75,"@type":76},"The study uses patient records with clinical indicators from the UCI Machine Learning Repository. It applies SMOTE for class balancing and uses ordinal encoding before training.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What performance metrics does the AdaBoost classification model achieve?",{"text":80,"@type":76},"AdaBoost achieves 92.98% accuracy with an AUROC of 0.97. Precision and recall vary by class, with higher performance for healthy individuals than for cirrhosis patients.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is early diagnosis of HCC in Hepatitis C patients important?",{"text":84,"@type":76},"Early diagnosis can raise the 5-year survival rate to over 70%, while diagnoses after metastasis reduce survival to below 16%. 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