[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117050-en":3,"doc-seo-117050-105":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},117050,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Integrating Machine Learning Methods For Medical Diagnosis - Doctoral Dissertation","A dissertation dedicated to enhancing and fine-tuning predictive models for medical diagnosis using a range of optimization techniques. The study develops an approach aimed at improving both accuracy and efficiency within data-driven healthcare workflows. It reviews data analysis and missing-value challenges, then builds and evaluates multiple machine learning methods, including neural network architectures, optimization strategies, and classification pipelines. Medical diagnosis applications cover datasets such as pediatric pneumonia chest X-rays and breast cancer, with reported results and future directions.","University of Texas at El Paso  \nScholarWorks@UTEP  \nOpen Access Theses & Dissertations  \n2023-12-01  \nIntegrating Machine Learning Methods For Medical Diagnosis  \nJazmin Quezada  \nUniversity of Texas at El Paso  \nFollow this and additional works at: [https://scholarworks.utep.edu/open_etd](https://scholarworks.utep.edu/open_etd)  \n Part of the Artificial Intelligence and Robotics Commons, Mathematics Commons, and the Statistics and Probability Commons  \nRecommended Citation  \nQuezada, Jazmin, \"Integrating Machine Learning Methods For Medical Diagnosis\" (2023) . Open Access Theses & Dissertations. 4014.  \n[https://scholarworks.utep.edu/open_etd/4014](https://scholarworks.utep.edu/open_etd/4014)  \nThis is brought to you for free and open access by ScholarWorks@UTEP. It has been accepted for inclusion in Open Access Theses & Dissertations by an authorized administrator of ScholarWorks@UTEP. For more information, please contact [lweber@utep.edu](lweber@utep.edu).  \nINTEGRATING MACHINE LEARNING METHODS FOR MEDICAL DIAGNOSIS  \nJAZMIN QUEZADA  \nDoctoral Program in Data Science  \nAPPROVED:  \n\n| Maria Christina Mariani, Chair, Ph.D. |\n| --- |\n| Osei Tweneboah, Ph.D., Co-Chair |\n| Joe A. Guthrie, Ph.D. |\n| Natasha S Sharma, Ph.D. |\n\nKristine M. Garza, Ph.D.  \nStephen Crites, Ph.D.  \nDean of the Graduate School  \n©Copyright by  \nJazmin Quezada 2023  \nTo my  \nFather & brother Michael, may your souls rest in peace, and to my mother; with the greatest of love.  \nINTEGRATING MACHINE LEARNING METHODS FOR MEDICAL DIAGNOSIS  \nby  \nJAZMIN QUEZADA  \nDISSERTATION  \nPresented to the Faculty of the Graduate School of The University of Texas at El Paso  \nin Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nDOCTOR OF PHILOSOPHY  \nDepartment of Mathematical Sciences  \nTHE UNIVERSITY OF TEXAS AT EL PASO  \nDecember 2023  \nTable of Contents  \nPage  \nTable of Contents ..................................... v  \nChapter  \n1 Introduction ...................................... 1  \n1.1 Data Analysis .................................. 6  \n1.2 Missing values .................................. 8  \n1.3 Motivation .................................... 10  \n2 Background Research ................................. 12  \n2.1 Los Alamos National Laboratory Research .................. 12  \n2.2 Introduction to missing data ......................... 13  \n2.3 Educational data description .......................... 14  \n2.4 Imputation methods .............................. 15  \n2.4.1 Amelia .................................. 17  \n2.4.2 missForrest ............................... 17  \n2.4.3 MI .................................... 27  \n2.4.4 Finding Missing Data with Neural Network ............. 30  \n2.4.5 Classification of Credit Card Default ................. 35  \n3 Methodologies ..................................... 41  \n3.1 Simple Neuron ................................. 42  \n3.1.1 Neuron With Vector Input ....................... 43  \n3.1.2 A Layer of Neurons ........................... 44  \n3.1.3 Multiple Layers of Neurons ....................... 45  \n3.1.4 Linear Regression ............................ 49  \n3.1.5 Logistic Regression ........................... 51  \n3.2 Convolutional Neural Networks (CNN) .................... 55  \n3.2.1 Support Vector Machines ........................ 58  \n3.2.2 k-Nearest Neighbors .......................... 59  \n3.3 Graph Neural Networks ............................ 61  \n3.4 Optimization Methods ............................. 69  \n3.4.1 Backpropagation (Gradient descent) and generalizations ...... 70  \n3.4.2 Least square method .......................... 73  \n3.4.3 Parameters and hyper-parameters of the model ........... 75  \n3.4.4 GridSearchCV .............................. 77  \n3.4.5 Optimization of function by using subsequent approximations and related predictions ........................... 78  \n3.4.6 Meta-Learning ............................. 80  \n3.4.7 Universal approximation theorem ................... 86  \n3.4.8 Epoch","cbCaigUGj5yxxMt6","https://ap.wps.com/l/cbCaigUGj5yxxMt6","pdf",3990802,1,151,"English","en",105,"# Introduction\n## Data Analysis\n## Missing values\n## Motivation\n# Background Research\n## Introduction to missing data\n## Imputation methods\n## Educational data description\n# Methodologies\n## Simple Neuron and regression models\n## Convolutional Neural Networks (CNN)\n## Graph Neural Networks\n## Optimization methods and training components\n## Activation functions and data splitting\n## Classification and regression methods\n# Medical Diagnosis\n## Data background: pediatric pneumonia chest X-ray data images\n## Breast cancer\n## Results\n# Conclusions\n## Future plans\n# Timeline\n# References\n# Curriculum Vitae","[{\"question\":\"What is the dissertation’s primary goal in medical diagnosis research?\",\"answer\":\"To enhance and fine-tune predictive models for medical diagnosis by applying various optimization techniques, improving diagnostic accuracy and efficiency.\"},{\"question\":\"How does the study address missing data?\",\"answer\":\"It covers data analysis, discusses missing values, and reviews imputation methods, including neural-network-based approaches for finding missing data.\"},{\"question\":\"Which medical diagnosis applications and datasets are used?\",\"answer\":\"The dissertation evaluates models on pediatric pneumonia chest X-ray data and breast cancer, including related subtopics such as dataset representations and prediction with kNN and support vector machines.\"}]","Integrating Machine Learning Methods For Medical Diagnosis - 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