[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117481-en":3,"doc-seo-117481-105":30,"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":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},117481,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Enhanced Diabetes Detection using Machine Learning and Deep Learning Techniques","This thesis presents an enhanced approach to diabetes detection by leveraging both machine learning and deep learning techniques. The work covers data manipulation, analysis, preprocessing, feature selection, visualization, and careful handling of imbalanced datasets through oversampling. It outlines dataset understanding, min-max scaling, oversampling via SMOTE, and feature engineering steps, followed by training, validation, and hyperparameter tuning. Multiple algorithms are evaluated, including decision trees, random forests, XGBoost, logistic regression, KNN, and deep learning models such as CNNs and RNNs, to improve predictive performance.","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: Enhanced Diabetes Detection using Machine Learning and Deep Learning Techniques  \nAUTHOR: Kripali Bhatodra  \nDATE OF SUCCESSFUL DEFENSE: November 16th, 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.  \nAhmad R. Hadaegh  \n\n| THESIS COMMITTEE CHAIR\u003Cbr>Sreedevi Gutta | SIGNATURE | DATE |\n| --- | --- | --- |\n\nTHESIS COMMITTEE MEMBER  \nSIGNATURE  \nDATE  \nENHANCED DIABETES DETECTION USING MACHINE LEARNING AND DEEP LEARNING TECHNIQUES  \nName: Kripali Bhatodra  \nContents  \nAcknowledgement ............................................................................................................................ 5  \nList of Figures ................................................................................................................................... 5  \nList of Tables ..................................................................................................................................... 5  \nAbstract.............................................................................................................................................. 5  \nChapter 1: Introduction..................................................................................................................... 6  \nAims and Objectives: - ................................................................................................................. 6  \nResearch Contributions: - ............................................................................................................. 7  \nChapter 2: Related Work .................................................................................................................. 8  \nChapter 3: Methodology................................................................................................................. 11  \n1 Libraries Used: - ....................................................................................................................... 13  \nData Manipulation and Analysis ............................................................................................ 13  \nData Visualization ................................................................................................................... 13  \nData Preprocessing and Feature Selection............................................................................. 13  \nHandling Imbalanced Dataset................................................................................................. 13  \nData Splitting and Model Selection ....................................................................................... 13  \n2. Dataset Information: - ............................................................................................................. 14  \n2.1. Features of the Dataset .................................................................................................... 14  \n2.2. Min Max Scaling.............................................................................................................. 16  \nFeature Scaling .................................................................................................................... 16  \nConvergence ........................................................................................................................ 16  \nInterpretability ..................................................................................................................... 17  \nVisualization ........................................................................................................................ 17  \n2.3. Oversampling ................................................................................................................... 17","cbCainxLk53BVRIH","https://ap.wps.com/l/cbCainxLk53BVRIH","pdf",657807,1,35,"English","en",105,"# Chapter 1: Introduction\n## Aims and Objectives\n## Research Contributions\n# Chapter 2: Related Work\n# Chapter 3: Methodology\n## Libraries Used\n## Data Manipulation and Analysis\n## Data Visualization\n## Data Preprocessing and Feature Selection\n## Handling Imbalanced Dataset\n## Data Splitting and Model Selection\n## Dataset Information\n## Features of the Dataset\n## Min Max Scaling\n## Oversampling (SMOTE)\n## Feature Engineering\n## Algorithms Used\n## Machine Learning Algorithms\n## Deep Learning Algorithms\n# Chapter 4: Analysis of the Results","[{\"question\":\"What problem does this thesis address?\",\"answer\":\"It focuses on improving diabetes detection by building predictive models using machine learning and deep learning techniques.\"},{\"question\":\"How does the methodology handle imbalanced datasets?\",\"answer\":\"It uses oversampling, specifically SMOTE implementation, to balance the training data before model learning.\"},{\"question\":\"Which algorithms are covered for modeling and evaluation?\",\"answer\":\"The thesis includes machine learning models such as decision trees, random forests, XGBoost, logistic regression, and KNN, along with deep learning models including convolutional and recurrent neural networks.\"}]","Enhanced Diabetes Detection using Machine Learning and Deep Learning Techniques | PDF",1785676100,88,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"enhanced-diabetes-detection-using-machine-learning-and-deep-learning-techniques","",{"@graph":36,"@context":86},[37,54,69],{"@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/enhanced-diabetes-detection-using-machine-learning-and-deep-learning-techniques/117481/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",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 problem does this thesis address?","Question",{"text":76,"@type":77},"It focuses on improving diabetes detection by building predictive models using machine learning and deep learning techniques.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the methodology handle imbalanced datasets?",{"text":81,"@type":77},"It uses oversampling, specifically SMOTE implementation, to balance the training data before model learning.",{"name":83,"@type":74,"acceptedAnswer":84},"Which algorithms are covered for modeling and evaluation?",{"text":85,"@type":77},"The thesis includes machine learning models such as decision trees, random forests, XGBoost, logistic regression, and KNN, along with deep learning models including convolutional and recurrent neural networks.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]