[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121379-en":3,"doc-seo-121379-105":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},121379,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predicting Cardiovascular Disease Risk through Machine Learning - A Comparative Study","This thesis develops and evaluates machine learning approaches for predicting cardiovascular disease risk through a structured comparative study. It covers the rationale for using machine learning in cardiovascular healthcare, the end-to-end study methodology, and the selection of effective models and algorithms. The work includes data understanding and exploration, preprocessing steps such as age transformation and label encoding, feature selection, and hyperparameter tuning across ensemble methods like XGBoost and Random Forest. Results emphasize algorithm comparison factors and model effectiveness.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nPREDICTING CARDIOVASCULAR DISEASE RISK THROUGH MACHINE LEARNING: A COMPARATIVE STUDY  \nA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science  \nby  \nKunlong Wang  \nDecember 2023  \nCopyright by Kunlong Wang 2023  \nThe thesis ofKunlong Wang is approved:  \n\n| Dr. Mahdi Ebrahimi |\n| --- |\n| Professor Felix Robinovich |\n\nDr. Li Liu, Chair  \nDate  \nDate  \nDate  \nCalifornia State University, Northridge  \nAcknowledgement  \nI would like to express my sincere gratitude to Dr. Li Liu, Dr. Mahdi Ebrahimi and Professor Felix Robinovich for their invaluable contributions to the refinement of this paper. Their insightful comments and constructive suggestions significantly enhanced the quality of my work. I am truly thankful for their time, expertise, and dedication to guiding me through the revision process. Their mentorship has been instrumental in shaping the content and improving the overall clarity of this paper. I am fortunate to have had the privilege of benefiting from their wisdom and scholarly guidance.  \nTable of Contents  \nCopyright Page............................................................................................................................................ ii  \nSignature Page ........................................................................................................................................... iii  \nAcknowledgement ..................................................................................................................................... iv  \nList of Tables ............................................................................................................................................ vii  \nList of Figures .......................................................................................................................................... viii  \nAbstract ...................................................................................................................................................... ix  \nChapter 1 Introduction ............................................................................................................................. 1  \n1.1 Objective ..................................................................................................................................... 2  \n1.2 Related Work .............................................................................................................................. 3  \n1.3 Outline......................................................................................................................................... 7  \nChapter 2 Methodology ........................................................................................................................... 9  \n2.1 Transformative Role of Machine Learning in Cardiovascular Healthcare ................................. 9  \n2.2 Methodology of this study ........................................................................................................ 10  \nChapter 3 Effective Model and Algorithm Selection for Cardiovascular Disease Prediction............... 12  \n3.1 Machine Learning Techniques for CVD Prediction ................................................................. 12  \n3.2 Ensemble Methods and Feature Importance ............................................................................. 12  \n3.3 Model and Algorithm Comparison Factors .............................................................................. 13  \nChapter 4 Implementation ..................................................................................................................... 14  \n4.1 Data Understanding and Exploration ........................................................................................ 14  \n4.1.1 Data Source .........................................................................................................","cbCaii7t5wuxgcrN","https://ap.wps.com/l/cbCaii7t5wuxgcrN","pdf",959307,1,49,"English","en",105,"# Chapter 1 Introduction\n## Objective\n## Related Work\n## Outline\n# Chapter 2 Methodology\n## Transformative Role of Machine Learning in Cardiovascular Healthcare\n## Methodology of this study\n# Chapter 3 Effective Model and Algorithm Selection for Cardiovascular Disease Prediction\n## Machine Learning Techniques for CVD Prediction\n## Ensemble Methods and Feature Importance\n## Model and Algorithm Comparison Factors\n# Chapter 4 Implementation\n## Data Understanding and Exploration\n## Data Preprocessing\n## Feature Selection\n## Hyperparameter Tuning\n# Chapter 5 Comparison\n# Chapter 6 Conclusion","[{\"question\":\"What is the main objective of this thesis?\",\"answer\":\"The thesis aims to predict cardiovascular disease risk using machine learning and to conduct a comparative evaluation of selected models and algorithms.\"},{\"question\":\"Which dataset preparation steps are described in the implementation?\",\"answer\":\"The study explains data understanding and exploration, followed by preprocessing including age transformation and label encoding, as well as feature selection.\"},{\"question\":\"How are machine learning models compared and tuned?\",\"answer\":\"The comparison focuses on factors for model and algorithm selection, while hyperparameter tuning is performed across algorithms such as XGBoost and Random Forest, supported by details of the hardware and libraries.\"}]","Predicting Cardiovascular Disease Risk through Machine Learning - 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