[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127878-en":3,"doc-seo-127878-105":31,"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":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},127878,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","COMPARATIVE ASSESSMENT OF MACHINE LEARNING AND DEEP LEARNING MODELS FOR DRUG EFFECTIVENESS USING SENTIMENT ANALYSIS - Master of Science Project","Online patient-generated drug reviews provide an unstructured source for evaluating drug effectiveness and patient satisfaction, and sentiment analysis enables extracting actionable insights. This culminating research project performs a comparative assessment of traditional Machine Learning and Deep Learning models using participant review sentiment. Models evaluated include SVM, XGBoost, Random Forest, LSTM, and BERT, with experiments conducted in a Python Kaggle environment using UCI datasets.","California State University, San Bernardino  \nCSUSB ScholarWorks  \n\n| Electronic Theses, Projects, and Dissertations | Office of Graduate Studies |\n| --- | --- |\n| 12-2024\u003Cbr>COMPARATIVE ASSESSMENT OF MACHINE LEARNING AND DEEP LEARNING MODELS FOR DRUG EFFECTIVENESS USING SENTIMENT ANALYSIS\u003Cbr>Blessing Ogechukwu Nwogu\u003Cbr>California State University-San Bernardino\u003Cbr>Follow this and additional works at: [https://scholarworks.lib.csusb.edu/etd](https://scholarworks.lib.csusb.edu/etd)\u003Cbr> Part of the Business Intelligence Commons, Health Information Technology Commons, and the Technology and Innovation Commons |  |\n\nRecommended Citation  \nNwogu, Blessing Ogechukwu, \"COMPARATIVE ASSESSMENT OF MACHINE LEARNING AND DEEP LEARNING MODELS FOR DRUG EFFECTIVENESS USING SENTIMENT ANALYSIS\" (2024) . Electronic Theses, Projects, and Dissertations. 2061.  \n[https://scholarworks.lib.csusb.edu/etd/2061](https://scholarworks.lib.csusb.edu/etd/2061)  \nThis Project is brought to you for free and open access by the Office of Graduate Studies at CSUSB ScholarWorks. It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [scholarworks@csusb.edu](scholarworks@csusb.edu).  \nCOMPARATIVE ASSESSMENT OF MACHINE LEARNING AND DEEP  \nLEARNING MODELS FOR DRUG EFFECTIVENESS USING SENTIMENT  \nANALYSIS  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nIn Partial Fulfillment of the Requirements for the Degree Master of Science in  \nInformation Systems and Technology: Business Intelligence and Analytics  \nby Blessing Nwogu December 2024  \nCOMPARATIVE ASSESSMENT OF MACHINE LEARNING AND DEEP  \nLEARNING MODELS FOR DRUG EFFECTIVENESS USING SENTIMENT  \nANALYSIS  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nby  \nBlessing Nwogu  \nDecember 2024  \nApproved by:  \nDr. Essia Hamouda , Committee Member, Chair  \nDr. Conrad Shayo, Committee Member, Reader & Chair, Information and  \nDecision Sciences Department  \n© 2024 Blessing Nwogu  \nABSTRACT  \nIn recent years, the proliferation of online patient-generated drug reviews has created a valuable resource for assessing drug effectiveness and patient satisfaction, with sentiment analysis emerging as a powerful tool for extracting insights from this unstructured data.  \nThis culminating research project conducted a comparative analysis of traditional Machine Learning (ML) and Deep Learning (DL) models for assessing drug effectiveness using sentiment analysis of participant reviews. The research aimed to evaluate the performance of Support Vector Machine (SVM), XGBoost, Random Forest, Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) models in this context. This culminating research project addressed three main research questions: (1) How do traditional ML models compare to each other in assessing drug effectiveness ratings? (2) How do DL models compare to each other in this assessment? (3) How do the performances of DL models compare to traditional ML methods? The data was sourced from UCI Machine Learning Repository and was analyzed using Python within the Kaggle environment.  \nBased on our analysis, for the first question, Random Forest demonstrated superior performance among traditional ML models, achieving 97% accuracy, followed by SVM (95%) and XGBoost (94%) . Random Forest excelled in precision for negative reviews (0 .99) and recall for positive reviews (0 .99), while SVM and XGBoost showed slightly better precision for positive  \nreviews (0 .97) . Regarding the second question, BERT outperformed LSTM in assessing drug effectiveness ratings. BERT achieved 86% accuracy compared to LSTM's 82% . BERT demonstrated higher precision, especially for positive reviews (0 .90), and better recall for negative reviews (0 .68) . BERT's F1 scores were consistently higher than LSTM's for both positi","cbCaiueRPoODLsbf","https://ap.wps.com/l/cbCaiueRPoODLsbf","pdf",479930,2,1,56,"English","en",105,"","[{\"question\":\"What is the main goal of the comparative study?\",\"answer\":\"To compare traditional machine learning and deep learning models for assessing drug effectiveness using sentiment analysis of participant reviews.\"},{\"question\":\"Which models are evaluated in the project?\",\"answer\":\"The study evaluates SVM, XGBoost, Random Forest, LSTM, and BERT for drug effectiveness rating assessment.\"},{\"question\":\"What do the results show about performance between ML and DL models?\",\"answer\":\"Random Forest achieves the highest overall accuracy among traditional ML models, while BERT outperforms LSTM among deep learning models; overall, traditional ML performs better on accuracy, precision, recall, and F1.\"}]","COMPARATIVE ASSESSMENT OF MACHINE LEARNING AND DEEP LEARNING MODELS FOR DRUG EFFECTIVENESS USING SENTIMENT ANALYSIS - Master of Science Project | PDF",1785942489,141,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":26,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":29},"comparative-assessment-of-machine-learning-and-deep-learning-models-for-drug-effectiveness-using-sentiment-analysis-master-of-science-project",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/comparative-assessment-of-machine-learning-and-deep-learning-models-for-drug-effectiveness-using-sentiment-analysis-master-of-science-project/127878/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-22","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 goal of the comparative study?","Question",{"text":75,"@type":76},"To compare traditional machine learning and deep learning models for assessing drug effectiveness using sentiment analysis of participant reviews.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which models are evaluated in the project?",{"text":80,"@type":76},"The study evaluates SVM, XGBoost, Random Forest, LSTM, and BERT for drug effectiveness rating assessment.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results show about performance between ML and DL models?",{"text":84,"@type":76},"Random Forest achieves the highest overall accuracy among traditional ML models, while BERT outperforms LSTM among deep learning models; 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