[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119452-en":3,"doc-seo-119452-105":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},119452,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","The Effectiveness of Machine Learning and Deep Learning to Detect Arrhythmia - Thesis","Early detection of arrhythmia using electrocardiography (ECG) supports timely diagnosis, prevention, and treatment of cardiovascular diseases. Machine learning methods can recognize heartbeat irregularities through supervised and unsupervised learning, yet performance is limited by the scarcity of publicly available datasets and privacy constraints on health data. This thesis addresses the challenge by combining advanced data preparation and feature engineering with a range of machine learning and deep learning algorithms to improve detection accuracy under privacy-aware data limitations.","CALIFORNIA STATE UNIVERSITY, NORTHRIDGE  \nTHE EFFECTIVENESS OF MACHINE LEARNING AND DEEP  \nLEARNING TO DETECT ARRHYTHMIA  \nA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science  \nby  \nManpreet Dhindsa  \nMay 2024  \nThe thesis of Manpreet Dhindsa is approved:  \nMahdi Ebrahimi, Ph.D. Date  \nVanessa Klotzman, Ph.D. Candidate Date  \nXunfei Jiang, Ph.D., Chair Date  \nCalifornia State University, Northridge  \nAcknowledgements  \nAccomplishing this thesis has been a journey brimming with challenges and enriching experiences, encompassing countless long study nights, persistent struggles, continued learning, and satisfying moments of triumph. I am profoundly grateful for the support and guidance of those who have played pivotal roles in helping me achieve this significant milestone.  \nFirst and foremost, I extend my deepest appreciation to my thesis advisor, Dr. Xunfei Jiang. From encouraging me to join the Boracle Project, to guiding my thesis with insightful guidance and constructive feedback, her expertise and steadfast support have been instrumental in the success of my project. I am genuinely thankful for her patience and unwavering encouragement.  \nI also want to acknowledge the Boracle Project at ARCS, the Autonomy Research Center for STEAHM funded by NASA, for providing the opportunity to collaborate with likeminded students from diverse fields of study. I am deeply appreciative of the collective brilliance and contributions of everyone involved.  \nI must also express heartfelt gratitude to my family, whose unwavering love and support deserve particular recognition. Their constant belief in me and my aspirations has propelled me forward, especially during the most challenging stages of my research.  \nLastly, I extend my thanks to MarketScan Information Systems Inc., particularly my manager Pavel Kovalev, for his support and understanding throughout this journey.  \nTable of Contents  \nSignature page ii  \nAcknowledgements iii  \nList of Tables v  \nList of Figures vi Abstract vii 1 Introduction 1  \n2 Related Works 3  \n2.1 Electrocardiogram Data ............................ 3  \n2.2 Machine Learning Models .......................... 4  \n2.3 Deep Learning Models ............................ 5  \n3 Design 7  \n3.1 Data Preparation and Feature Engineering .................. 7  \n3.2 Machine/Deep Learning Algorithms ..................... 9  \n3.3 Evaluation Metrics .............................. 9  \n4 Methods 11  \n4.1 Support Vector Machine (SVM) ....................... 11  \n4.2 Random Forest (RF) ............................. 11  \n4.3 Convolutional Neural Network (CNN) .................... 13  \n4.4 Ensemble Learning (EL) ........................... 13  \n5 Results 15  \n5.1 Dataset .................................... 15  \n5.2 Data Preparation and Feature Engineering .................. 16  \n5.2.1 Missing Value Handling ....................... 16  \n5.2.2 Label Encoding ............................ 16  \n5.2.3 Scaling to Binary Classification ................... 16  \n5.2.4 Removing Bias ............................ 16  \n5.2.5 Dimensionality Reduction ...................... 18  \n5.2.6 Feature Selection ........................... 18  \n5.3 Machine and Deep Learning Models ..................... 20  \n5.4 Evaluation ................................... 22  \n6 Conclusion 24  \nList of Tables  \n5.1 SVM and RF Model Evaluation using PCA for Dimensionality Reduction . 18  \n5.2 SVM and RF Model Evaluation Results using Recursive Feature Elimination 20  \n5.3 SVM, RF, CNN, and EL Model Evaluation Results (Best Case) ....... 21  \nList of Figures  \n2.1 ECG Waveform [1] ............................... 4  \n3.1 Design of the Project ............................. 7  \n3.2 Data Preparation and Feature Engineering Flowchart ............ 8  \n4.1 SVM Model Illustration [2] .......................... 12  \n4.2 RF Model Illustration [2] ............................ 12  \n4.3 CNN Model Illustration [2] ..........","cbCaimZIwPg6rvnu","https://ap.wps.com/l/cbCaimZIwPg6rvnu","pdf",1052700,1,33,"English","en",105,"# Table of Contents\n## Acknowledgements\n## List of Tables\n## List of Figures\n## Abstract\n## Introduction\n## Related Works\n## Design\n## Methods\n## Results\n## Conclusion","[{\"question\":\"Why is early arrhythmia detection important in this thesis?\",\"answer\":\"Early detection of arrhythmia via ECG helps enable timely detection, prevention, and treatment of cardiovascular diseases.\"},{\"question\":\"What key challenge does the thesis identify for machine learning arrhythmia detection?\",\"answer\":\"A major limitation is the lack of publicly available data, driven by concerns around patient health data privacy.\"},{\"question\":\"How does the project aim to improve arrhythmia detection accuracy?\",\"answer\":\"It uses advanced data preparation and feature engineering together with multiple machine learning and deep learning algorithms, then evaluates performance using defined evaluation metrics.\"}]","The Effectiveness of Machine Learning and Deep Learning to Detect Arrhythmia - 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