[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124778-en":3,"doc-seo-124778-105":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},124778,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Application of Machine Learning for Risk Determination in Cancer Occurrence and Recurrence - Dissertation","This dissertation applies machine learning methods to determine individualized risk for cancer occurrence and cancer recurrence, integrating clinical and sociodemographic information. It investigates how inheritance-related factors relate to recurrence risk and develops chromosome-scale length variability-based genetic risk scores to support prediction of cancer occurrence. The work reviews statistical and model limitations in cancer risk estimation, then studies multiple supervised and unsupervised learning approaches, including ensemble and deep learning strategies. Findings aim to improve risk estimation for cancer research and clinical decision-making.","UC Irvine  \nUC Irvine Electronic Theses and Dissertations  \nTitle  \nApplication of Machine Learning for Risk Determination in Cancer Occurrence and Recurrence  \nPermalink  \n[https://escholarship.org/uc/item/0v4016m1](https://escholarship.org/uc/item/0v4016m1)  \nAuthor  \nFatapour, Yasaman  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA,  \nIRVINE  \nApplication of Machine Learning for Risk Determination in Cancer Occurrence and  \nRecurrence  \nDISSERTATION  \nsubmitted in partial satisfaction of the requirements  \nfor the degree of  \nDOCTOR OF PHILOSPHY  \nin Biomedical Engineering  \nby  \nYasaman Fatapour  \nDissertation Committee:  \nAssociate Professor James P. Brody, Chair Professor William C. Tang  \nAssociate Professor Edward Kuan  \n© 2023 Yasaman Fatapour  \nDEDICATION  \nTo my husband Adam Abiri, my best friend and unwavering support,  \nTo my parents Maryam Tabatabai and Mohsen Fatapour who have sacrificed daily for me and are thousands of miles away,  \nTo my grandmother Parvin and my aunt Susan whose love have guided me,  \nTo my beloved siblings, Amirhossein and Sarvenaz  \nAnd to all the people who are fighting against cancer,  \nThis dissertation is dedicated to you all.  \nTABLE OF CONTENTS  \nList of [Figures ..................................................................................................................................vi](Figures ..................................................................................................................................vi)  \n[List of Tables ...............................](List of Tables ...............................).................................................................................................... xi  \nAcknowledgments......................................................................................................................... xiii  \nVITA ............................................................................................................................................... xiv  \nAbstract of The Dissertation .......................................................................................................... xv  \nChapter 1: Introduction .................................................................................................................. 1  \nChapter 2: Objectives &Specific Aims............................................................................................. 5  \nObjective 1: Assessing the Risk of Cancer Recurrence using Clinical and Sociodemographic Variables ...................................................................................................................................... 5  \nObjective 2: Investigating the Impact of Inheritance Factors on Cancer Recurrence Risk......... 6  \nObjective 3: Developing Chromosome Scale Length Variability-Based Genetic Risk Scores for Predicting Cancer Occurrence ..................................................................................................... 7  \nChapter 3: Background ................................................................................................................... 9  \nCancer Statistics .......................................................................................................................... 9  \nCancer Recurrence .................................................................................................................... 10  \nCancer Risk Estimate Models and Their Limitations ................................................................. 11  \nArtificial Intelligence.................................................................................................................. 11  \nSupervised learning ............................................................................................................... 13  \nUnsupervised learning ...............","cbCain3NzSKH3Z0E","https://ap.wps.com/l/cbCain3NzSKH3Z0E","pdf",3925818,1,155,"English","en",105,"# Chapter 1: Introduction\n# Chapter 2: Objectives &Specific Aims\n## Objective 1: Assessing the Risk of Cancer Recurrence using Clinical and Sociodemographic Variables\n## Objective 2: Investigating the Impact of Inheritance Factors on Cancer Recurrence Risk\n## Objective 3: Developing Chromosome Scale Length Variability-Based Genetic Risk Scores for Predicting Cancer Occurrence\n# Chapter 3: Background\n## Cancer Statistics\n## Cancer Recurrence\n## Cancer Risk Estimate Models and Their Limitations\n## Artificial Intelligence\n## Supervised learning\n## Unsupervised learning\n## Reinforcement learning\n## Machine Learning Algorithms\n## Application of Machine Learning in Cancer Research\n# Chapter 4: Assessing the Risk of Cancer Recurrence using Clinical and Sociodemographic Variables\n## 4.1: SEER Dataset: Identifying Cancer Recurrence Cases\n## 4.2: Machine Learning Model Development Using H2O.ai","[{\"question\":\"What is the main goal of the dissertation?\",\"answer\":\"The dissertation aims to use machine learning to determine risk for cancer occurrence and recurrence using clinical, sociodemographic, and genetic information.\"},{\"question\":\"How does the work evaluate recurrence risk?\",\"answer\":\"It assesses recurrence risk using clinical and sociodemographic variables, including identification of recurrence cases from the SEER dataset and model development with H2O.ai.\"},{\"question\":\"What genetic approach is developed for predicting cancer occurrence?\",\"answer\":\"It develops chromosome-scale length variability-based genetic risk scores to support prediction of cancer occurrence.\"}]","Application of Machine Learning for Risk Determination in Cancer Occurrence and Recurrence - Dissertation | 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