[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120397-en":3,"doc-seo-120397-105":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},120397,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning Paradigm for Comparative Techniques to Classifying Diabetes Data - Thesis","Diabetes is a metabolic illness that arises when the body cannot produce sufficient insulin or use it effectively. Untreated or undiagnosed diabetes can lead to severe complications, including heart disease, visual impairment, and kidney failure. This thesis compares seven machine learning algorithms for diabetes prediction using two datasets: Diabetes Dataset 2019 (DD2019) and Pima Indians Diabetes Dataset (PIDD). Performance is evaluated with Precision, Accuracy, F1-Score, AUC, and Recall; Random Forest and Gradient Boosting reach the highest accuracy on PIDD, while Random Forest and Artificial Neural Networks perform best on DD2019.","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  \nTITLE: MACHINE LEARNING PARADIGM FOR COMPARATIVE TECHNIQUES TO CLASSIFYING  \nDIABETES DATA  \nAUTHOR: SRI HARSHA BASANI  \nDATE OF SUCCESSFUL DEFENSE: 04-07-2023  \nTHE THESIS HAS BEEN ACCEPTED BY THE THESIS COMMITTEE IN  \nPARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE.  \nDR. SREEDEVI GUTTA  \n\n| THESIS COMMITTEE CHAIR\u003Cbr>DR. NAHID EBRAHIMI MAJD |\n| --- |\n| THESIS COMMITTEE MEMBER |\n\nTHESIS COMMITTEE MEMBER  \n\n| SIGNATURE |\n| --- |\n| SIGNATURE |\n\nSIGNATURE  \n\n| DATE |\n| --- |\n| DATE |\n\nDATE  \nTitle page  \nMACHINE LEARNING PARADIGM FOR COMPARATIVE TECHNIQUES TO CLASSIFYING DIABETES  \nDATA  \nACKNOWLEDGMENT  \nI would like to express my sincere gratitude to my advisor Dr. Sreedevi Gutta for the continuous support of my MS study and research, and for her patience, motivation, enthusiasm, and immense knowledge. Her guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better advisor and mentor for my research study.  \nI would like to thank my committee member Professor Nahid Ebrahimi Majd who has promoted critical thinking and helped me to see the research potential for further discoveries.  \nABSTRACT  \nDiabetes is a metabolic illness that appears when the body cannot produce enough insulin or utilize it properly. If diabetes is ignored and undiagnosed, numerous complications develop such as heart disease, visual impairment, and kidney failure. The application of machine learning (ML) techniques has increased recently in several industries, including business, healthcare, education, and recommendation systems. This study compares the outcomes of seven machine learning algorithms used to predict diabetes using experimental data. The seven algorithms under consideration are Logistic Regression, Support Vector Classifier, Decision Tree, Random Forest, Gradient Boosting, ADABoost, and Artificial Neural Network. Experiments are performed on two widely used datasets namely Diabetes Dataset 2019 (DD2019) and Pima Indians Diabetes Dataset (PIDD). The effectiveness of each of the seven algorithms is assessed using a variety of metrics, including Precision, Accuracy, F1-Score, AUC, and Recall. According to the results, Random Forest and Gradient Boosting achieved the highest accuracy of 92% on PIDD. Whereas on DD2019, Random Forest and Artificial Neural Networks achieved the highest accuracy of 97% .  \nTABLE OF CONTENT  \nACKNOWLEDGMENT ........................................................................................................................ ii  \nABSTRACT..................................................................................................................................... iii  \nCHAPTER 1: INTRODUCTION.............................................................................................................. 1  \n1.1 Problem Statement .................................................................................................................................. 1  \n1.2 Purpose of the Study and Motivation ......................................................................................................2  \n1.3 Research Methodology ............................................................................................................................2  \n1.4 Research Scope ........................................................................................................................................2  \nCHAPTER 2: RELATED WORK ............................................................................................................ 3  \nCHAPTER 3: METHODOLOGY............................................................................................................. 6  \n3.1 Dataset .....................................","cbCainjFEdahPh3o","https://ap.wps.com/l/cbCainjFEdahPh3o","pdf",717670,1,34,"English","en",105,"# Acknowledgment\n# Abstract\n# Chapter 1: Introduction\n## Problem Statement\n## Purpose of the Study and Motivation\n## Research Methodology\n## Research Scope\n# Chapter 2: Related Work\n# Chapter 3: Methodology\n## Dataset\n## Data preprocessing\n## Proposed Approach\n## Machine Learning Techniques\n# Chapter 4: Analysis of the Results\n## Evaluation metrics\n## Results\n## Discussion\n# Chapter 5: Conclusion\n# References\n# List of the Figures","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis addresses diabetes prediction and classification, motivated by the serious complications that arise when diabetes is ignored or undiagnosed.\"},{\"question\":\"Which machine learning algorithms are compared?\",\"answer\":\"Seven algorithms are compared: Logistic Regression, Support Vector Classifier, Decision Tree, Random Forest, Gradient Boosting, ADABoost, and Artificial Neural Network.\"},{\"question\":\"Which datasets and evaluation metrics are used?\",\"answer\":\"Experiments use Diabetes Dataset 2019 (DD2019) and Pima Indians Diabetes Dataset (PIDD). Effectiveness is assessed using Precision, Accuracy, F1-Score, AUC, and Recall.\"}]","Machine Learning Paradigm for Comparative Techniques to Classifying Diabetes Data - Thesis | PDF",1785729822,86,{"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-paradigm-for-comparative-techniques-to-classifying-diabetes-data-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/machine-learning-paradigm-for-comparative-techniques-to-classifying-diabetes-data-thesis/120397/",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","2026-08-03",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 addresses diabetes prediction and classification, motivated by the serious complications that arise when diabetes is ignored or undiagnosed.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms are compared?",{"text":80,"@type":76},"Seven algorithms are compared: Logistic Regression, Support Vector Classifier, Decision Tree, Random Forest, Gradient Boosting, ADABoost, and Artificial Neural Network.",{"name":82,"@type":73,"acceptedAnswer":83},"Which datasets and evaluation metrics are used?",{"text":84,"@type":76},"Experiments use Diabetes Dataset 2019 (DD2019) and Pima Indians Diabetes Dataset (PIDD). Effectiveness is assessed using Precision, Accuracy, F1-Score, AUC, and Recall.","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"]