[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126252-en":3,"doc-seo-126252-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126252,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Enhancing Liver Disease Diagnosis with Machine Learning: An Empirical Evaluation of Algorithms on the Indian Liver Patient Dataset - Thesis","Liver disease is a critical global health concern with significant morbidity and mortality. Early and accurate diagnosis is essential for effective treatment and management. This thesis uses machine learning to improve liver disease diagnosis with the Indian Liver Patient Dataset (ILPD). It performs data preprocessing and exploratory data analysis to ensure integrity and extract initial insights, then applies and evaluates interpretable classifiers, including decision trees, neural networks, K-nearest neighbors, and gradient boosting, using accuracy, precision, recall, and F1-score after SMOTE. Cross-validation and hyperparameter tuning support generalizable results.","CALIFORNIA STATE UNIVERSITY SAN MARCOS  \nTHESIS SIGNATURE PAGE  \nTHESIS SUBMITTED IN PARTIAL FULFILLMENT  \nOF THE REQUIREMENTS FOR THE DEGREE  \nMASTER OF SCIENCE  \nIN  \nCOMPUTER SCIENCE  \nThesis TITLE: Enhancing Liver Disease Diagnosis with Machine Learning: An Empirical Evaluation of Algorithms on the Indian Liver Patient Dataset  \nAUTHOR: Yashaswini Prakash  \nDATE OF SUCCESSFUL DEFENSE:  \nTHE THESIS HAS BEEN ACCEPTED BY THE THESIS COMMITTEE IN  \nPARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCSIENCE.  \nDr. Ahmed Hadaegh  \nTHESIS COMMITTEE CHAIR  \nDr. Sreedevi Gutta  \nTHESIS COMMITTEE MEMBER  \nSIGNATURE  \nDATE  \nEnhancing Liver Disease Diagnosis with Machine Learning: An Empirical Evaluation of Algorithms on the Indian Liver Patient Dataset  \nBy:  \nYashaswini Prakash  \nDate: May 2024….  \nTable of Contents  \nTable of Contents .............................................................................................................................. 3  \nAcknowledgement ............................................................................................................................ 4  \nAbstract.............................................................................................................................................. 5  \n1. Introduction .............................................................................................................................. 6  \n2. Related Work ............................................................................................................................ 6  \n3. Dataset....................................................................................................................................... 8  \nDataset for proposed method:....................................................................................................... 8  \nDataset used to compare the proposed methodology of tuned methods with the prior work for fair comparison:....................................................................................................................... 8  \n3.1. Features: ........................................................................................................................... 8  \n4. Exploratory Data Analysis ....................................................................................................... 9  \n5. Methodology........................................................................................................................... 12  \n5.1. Data Preprocessing: ....................................................................................................... 13  \n5.2. Over Sampling ............................................................................................................... 14  \n5.3. Cross-Validation:............................................................................................................ 15  \n5.4. Algorithms Used: ........................................................................................................... 15  \n5.5. Model Evaluation:.......................................................................................................... 16  \n5.6. Hyperparameter Tuning:................................................................................................ 16  \n6. Results And Analysis ............................................................................................................. 17  \n7. Analysis: ................................................................................................................................. 19  \n8. Conclusion .............................................................................................................................. 20  \n9. Future Work ............................................................................................................................ 21  \nReferences .................................................................","cbCaidNndfIL9P9n","https://ap.wps.com/l/cbCaidNndfIL9P9n","pdf",642557,4,1,24,"English","en",105,"# Acknowledgement\n# Abstract\n# Introduction\n# Related Work\n# Dataset\n## Dataset for proposed method\n## Dataset used for fair comparison\n# Exploratory Data Analysis\n# Methodology\n## Data Preprocessing\n## Over Sampling\n## Cross-Validation\n## Algorithms Used\n## Model Evaluation\n## Hyperparameter Tuning\n# Results And Analysis\n# Analysis\n# Conclusion\n# Future Work\n# References","[{\"question\":\"What dataset is used to train and evaluate the machine learning models?\",\"answer\":\"The thesis uses the Indian Liver Patient Dataset (ILPD). It also applies SMOTE to address class balance before evaluating performance.\"},{\"question\":\"Which machine learning algorithms are evaluated for liver disease diagnosis?\",\"answer\":\"The study evaluates decision trees, neural networks, K-nearest neighbors, and gradient boosting classifiers, with emphasis on interpretability for medical diagnostic use.\"},{\"question\":\"How are model performance and generalization assessed?\",\"answer\":\"Performance is measured using accuracy, precision, recall, and particularly the F1-score. Hyperparameter tuning and cross-validation are used to improve generalizability.\"}]","Enhancing Liver Disease Diagnosis with Machine Learning: An Empirical Evaluation of Algorithms on the Indian Liver Patient Dataset - Thesis | PDF",1785904062,60,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"enhancing-liver-disease-diagnosis-with-machine-learning-an-empirical-evaluation-of-algorithms-on-the-indian-liver-patient-dataset-thesis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/enhancing-liver-disease-diagnosis-with-machine-learning-an-empirical-evaluation-of-algorithms-on-the-indian-liver-patient-dataset-thesis/126252/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What dataset is used to train and evaluate the machine learning models?","Question",{"text":76,"@type":77},"The thesis uses the Indian Liver Patient Dataset (ILPD). It also applies SMOTE to address class balance before evaluating performance.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms are evaluated for liver disease diagnosis?",{"text":81,"@type":77},"The study evaluates decision trees, neural networks, K-nearest neighbors, and gradient boosting classifiers, with emphasis on interpretability for medical diagnostic use.",{"name":83,"@type":74,"acceptedAnswer":84},"How are model performance and generalization assessed?",{"text":85,"@type":77},"Performance is measured using accuracy, precision, recall, and particularly the F1-score. Hyperparameter tuning and cross-validation are used to improve generalizability.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":30,"slug":109},5,"Comic","comic",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]