[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119447-en":3,"doc-seo-119447-105":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},119447,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Enhancing Heart Disease Prediction through Machine Learning - A Comprehensive Approach - Master of Science Thesis","Heart disease remains a leading global cause of death, making earlier detection and accurate risk forecasting essential for improving treatment outcomes and reducing healthcare costs. This thesis investigates heart disease prediction by applying machine learning to patient data, incorporating key variables such as age, gender, blood pressure, cholesterol levels, and other health indicators. Multiple models are compared, including Decision Tree, KNN, Gradient Boosting, SVM, Random Forest, Logistic Regression, MLP neural networks, and Gaussian Naive Bayes, after data collection and preprocessing. Model quality is evaluated using F1 score and ROC-AUC.","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 Heart Disease Prediction through Machine Learning: A comprehensive Approach.  \nAUTHOR: Muni Venkatesh Chinthagumpala  \nDATE OF SUCCESSFUL DEFENSE: MARCH 14, 2025  \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  \nEnhancing Heart Disease Prediction throughMachine Learning: A comprehensive Approach  \nBy: Chinthagumpala Muni Venkatesh California State University San Marcos  \nTable of Contents  \nTable of Contents ............................................................................................................................ 3  \nAbstract ............................................................................................................................................ 4  \n1. Introduction ............................................................................................................................. 5  \n2. Related work................................................................................................................................ 8  \n3. Methodology .............................................................................................................................. 10  \n3.1 Data collection ................................................................................................................. 11  \n3.2 Data Preprocessing .......................................................................................................... 11  \n3.3 Model Selection ............................................................................................................... 12  \n3.4 Model Training and Hyperparameter Tuning ................................................................ 12  \n3.5 Model Evaluation ............................................................................................................ 12  \n3.6 Model Selection and Validation ...................................................................................... 13  \n3.7 Cross-Validation: ............................................................................................................. 13  \n3.8 Algorithms Used:............................................................................................................. 14  \n4. Model Evaluation: .................................................................................................................... 15  \n4.1 Hyperparameter Tuning: ................................................................................................. 16  \n4.2 Model evaluation ............................................................................................................. 17  \n4.3 Hyperparameter Tuning: ................................................................................................. 18  \n4.1 Performance metric¶: ...................................................................................................... 18  \n4.2 Confusion matrix : ........................................................................................................... 19  \n5. Dataset..................................................................................................................................... 20  \n6. Exploratory Data Analysis ...................................................................................................... 21  \n7 .Results : ...................................................................................................................................... 24  \n7.1 Comparing overall results on the datasets with the proposed results:..............","cbCaiuNrAVc7CTNQ","https://ap.wps.com/l/cbCaiuNrAVc7CTNQ","pdf",844439,1,29,"English","en",105,"# Introduction\n# Related work\n# Methodology\n## Data collection\n## Data Preprocessing\n## Model Selection\n## Model Training and Hyperparameter Tuning\n## Model Evaluation\n## Cross-Validation\n# Dataset\n# Exploratory Data Analysis\n# Results\n## Comparing overall results on the datasets\n## Analysis\n## Discussion\n## Consequences and Future Plans\n# Future work and conclusion\n# References","[{\"question\":\"What problem does the thesis address in heart disease care?\",\"answer\":\"It addresses the need for early detection and more precise risk prediction to improve treatment results and reduce healthcare costs.\"},{\"question\":\"Which machine learning models are used for heart disease prediction?\",\"answer\":\"The study evaluates Decision Tree, KNN, Gradient Boosting, SVM, Random Forest, Logistic Regression, MLP neural network, and Gaussian Naive Bayes.\"},{\"question\":\"How is model performance assessed in this research?\",\"answer\":\"Performance is evaluated using F1 score and the area under the ROC curve (ROC-AUC), with Decision Tree and Gradient Boosting reported as top performers.\"}]","Enhancing Heart Disease Prediction through Machine Learning - A Comprehensive Approach - Master of Science Thesis | PDF",1785724326,73,{"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},"enhancing-heart-disease-prediction-through-machine-learning-a-comprehensive-approach-master-of-science-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/enhancing-heart-disease-prediction-through-machine-learning-a-comprehensive-approach-master-of-science-thesis/119447/",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 in heart disease care?","Question",{"text":75,"@type":76},"It addresses the need for early detection and more precise risk prediction to improve treatment results and reduce healthcare costs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used for heart disease prediction?",{"text":80,"@type":76},"The study evaluates Decision Tree, KNN, Gradient Boosting, SVM, Random Forest, Logistic Regression, MLP neural network, and Gaussian Naive Bayes.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance assessed in this research?",{"text":84,"@type":76},"Performance is evaluated using F1 score and the area under the ROC curve (ROC-AUC), with Decision Tree and Gradient Boosting reported as top performers.","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"]