[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119959-en":3,"doc-seo-119959-105":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":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},119959,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Predicting Hypertension with Add Health Dataset using Machine Learning Models - Master’s Thesis","High blood pressure remains a prevalent global health challenge, and identifying contributing factors is essential for effective prevention and management. This thesis examines how sex, hereditary factors, habitats, and BMI influence the risk of high blood pressure using machine learning methods. Logistic Regression, Decision Trees, Random Forests, XGBoost, Support Vector Machines, and Neural Networks are trained on the public-use Add Health dataset to model and compare predictive performance for the target outcome.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nPredicting Hypertension with Add Health Dataset using Machine Learning Models  \nPermalink  \n[https://escholarship.org/uc/item/9nc9382p](https://escholarship.org/uc/item/9nc9382p)  \nAuthor  \nFan, Zihan  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA Los Angeles  \nPredicting Hypertension with Add Health Dataset using Machine Learning Models  \nA thesis submitted in partial satisfaction of the requirements for the degree Master of Applied Statistics and Data Science  \nby  \nZihan Fan  \n2024  \n© Copyright by Zihan Fan  \n2024  \nABSTRACT OF THE THESIS  \nPredicting Hypertension with Add Health Dataset  \nusing Machine Learning Models  \nby  \nZihan Fan  \nMaster of Applied Statistics and Data Science  \nUniversity of California, Los Angeles, 2024  \nProfessor Yingnian Wu, Chair  \nHigh blood pressure is a prevalent health concern worldwide, and identifying the factors that contribute to its development is crucial for prevention and management strategies. This study aimed to investigate the influence of sex, hereditary factors, habitats, and BMI on the risk of high blood pressure using machine learning techniques. Several models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, Support Vector Machines, and Neural Networks are employed on the public-use sample from the Add Health dataset.  \nThe thesis of Zihan Fan is approved.  \nMaria Cha Frederic Paik Schoenberg Guang Cheng Yingnian Wu, Committee Chair  \nUniversity of California, Los Angeles 2024  \nTo my family   who always support me  \niv  \nTABLE OF CONTENTS  \n1 Introduction ...................................... 1  \n2 Data .......................................... 3  \n2.1 Data Introduction ................................. 3  \n2.2 Data Preparation ................................. 5  \n3 Models ......................................... 7  \n3.1 Logistic Regression ................................ 7  \n3.2 Decision tree and Random Forest ........................ 11  \n3.2.1 Decision Tree ............................... 11  \n3.2.2 Random Forest .............................. 13  \n3.3 XGBoost ...................................... 16  \n3.4 Support Vector Machines(SVM) ......................... 19  \n3.5 Neural Networks ................................. 23  \n4 Results ......................................... 27  \n5 Conclusion ....................................... 30  \nReferences ......................................... 32  \nLIST OF FIGURES  \n2.1 Frequency of High Blood Pressure before resampling ............... 5  \n3.1 Importance rank with XGBoost ........................... 17  \n3.2 Importance rank with SVM ............................. 20  \n3.3 Importance rank with Neural Network ....................... 24  \nLIST OF TABLES  \n2.1 Class distribution of High Blood Pressure after SMOTE ............. 6  \n3.1 Logistic Regression Coefficients ........................... 9  \n3.2 Classification Report for Decision Tree ....................... 12  \n3.3 Classification Report for Random Forest ...................... 14  \n4.1 Model Performance Comparison ........................... 28  \nCHAPTER 1  \nIntroduction  \nI was inspired to work on this specific topic because of my personal experience. During a recent annual medical examination, my 25-year-old friend received a warning from her doctor. The doctor cautioned that if she did not make significant lifestyle changes, she would likely develop diabetes and high blood pressure in the near future. This news was alarming, given her young age.  \nAround the same time, I had a routine blood test. As the nurse drew my blood, I noticed a distinct yellow substance in the sample tube, which the nurse identified as fat. This immediately sparked concern about my own health, as I feared my triglyceride levels would be dangerously high due to my","cbCaip1D8S3klpca","https://ap.wps.com/l/cbCaip1D8S3klpca","pdf",775154,1,41,"English","en",105,"# Introduction\n# Data\n## Data Introduction\n## Data Preparation\n# Models\n## Logistic Regression\n## Decision tree and Random Forest\n### Decision Tree\n### Random Forest\n## XGBoost\n## Support Vector Machines (SVM)\n## Neural Networks\n# Results\n# Conclusion\n# References","[{\"question\":\"Which factors are analyzed for high blood pressure risk in the thesis?\",\"answer\":\"The thesis evaluates the influence of sex, hereditary factors, habitats, and BMI on the risk of high blood pressure.\"},{\"question\":\"What machine learning models are used to predict hypertension?\",\"answer\":\"Models include Logistic Regression, Decision Trees, Random Forests, XGBoost, Support Vector Machines, and Neural Networks.\"},{\"question\":\"What dataset supports the study and how is it described?\",\"answer\":\"The models are trained using the public-use sample from the Add Health dataset.\"}]","Predicting Hypertension with Add Health Dataset using Machine Learning Models - 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