[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123701-en":3,"doc-seo-123701-105":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},123701,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Treatment Outcome Prediction in Locally Advanced Cervical Cancer - A Machine Learning Approach using Feature Selection on Multi-Source Data","Cervical cancer is a major global health concern, and predicting treatment outcomes can support more informed clinical decisions. Machine learning enables analysis of complex relationships, pattern discovery, and integration of heterogeneous information sources. This thesis targets key challenges in cervical cancer outcome prediction, including limited dataset size and variable data quality, differences across datasets, the need to identify informative features among clinical, imaging, and molecular data, and class imbalance that can bias results. A multi-source pipeline and models are developed using RENT for feature selection and SMOTE for imbalance handling.","Master’s Thesis 2023 30 ECTS  \nFaculty of Science and Technology  \nTreatment Outcome Prediction in Locally Advanced Cervical Cancer:  \nA Machine Learning Approach using Feature Selection on Multi-Source Data  \nSina Rokhideh Data Science  \nI  \nAcknowledgements  \nI would like to express my sincere appreciation to the individuals who have assisted me during my academic journey and the completion of my master’s degree:  \nFirst and foremost, I am deeply grateful to my thesis supervisor, Stefan Schrunner, for his unwavering guidance, encouragement, and support throughout my research. His invaluable insights and constructive feedback during our regular meetings have significantly influenced and shaped my research. I am truly thankful for his mentorship.  \nI would also like to extend my profound gratitude to my co-supervisors, Oliver Tomic and Cecilia Marie Futsæther, for sharing their knowledge and experiences with me. Their valuable perspectives and constructive criticism have helped refine my research approach and methodology.  \nI want to acknowledge the REALTEK faculty and staff at the Norwegian University of Life Sciences (NMBU) for providing a supportive academic environment and abundant knowledge and resources that have greatly assisted me in my studies.  \nLastly, I would like to express my heartfelt thanks to my wife, Helia, for her unwavering love, support, and encouragement throughout my master’s program. Without her constant understanding, patience, and selfless support, I would not have been able to complete this academic journey. I also want to convey my deep gratitude to my family, especially my mother, who has been a pillar of support from a distance. Her teachings have played a crucial role in shaping my mindset and inspiring me to remain motivated and diligent in pursuing my goals.  \nSina Rokhideh ˚As, Norway-July 17, 2023  \nIII  \nAbstract  \nCancer is a significant global health issue, and cervical cancer, one of the most common types among women, has far-reaching impacts worldwide. Researchers are studying cervical cancer from various perspectives, conducting thorough investigations, and utilizing novel technologies to gain a deeper understanding of the disease and its risk factors. Machine learning has increasingly found applications in cancer research due to its ability to analyze complex data relationships, recognize patterns, adapt to new information, and integrate with other technologies. By harnessing predictive machine learning models to anticipate treatment outcomes before commencing any therapies, healthcare providers might be able to make more informed decisions, allocate resources effectively, and provide personalized care.  \nDespite significant efforts in the scientific community, the development of accurate machine learning models for cervical cancer treatment outcome prediction faces several open challengesand unresolved questions. A major challenge in developing accurate prediction models is the limited availability and quality of data. The quantity and quality of data differ across various datasets, which can significantly affect the performance and applicability of machine learning models. Additionally, it is crucial to identify the most informative and relevant features from diverse data sources, including clinical, imaging, and molecular data, to ensure accurate outcome prediction. Moreover, cancer datasets often suffer from class imbalance. Addressing this issue is another essential step to prevent biased predictions and enhance the overall performance of the models.  \nThis study aims to improve the prediction of treatment outcomes in patients with locally advanced cervical cancer by utilizing a multi-source dataset and developing different machinelearning models. The dataset includes various data sources, such as medical images, gene scores, and clinical data. A preprocessing pipeline is developed to optimize the data for training machine-learning models. The Repeated Elastic Net Technique (RENT)","cbCail17XaQS0OBJ","https://ap.wps.com/l/cbCail17XaQS0OBJ","pdf",7333464,1,115,"English","en",105,"# Introduction\n## Motivation and Background\n## Objective\n## Structure of the Thesis\n# Theory\n## Medical Imaging\n## Pharmacokinetic Analysis\n## Clinical Examination and Gene Expression\n## Machine Learning\n### Learning Techniques and Algorithms\n### ML and Medical Imaging\n### Overfitting\n## Classification Models\n### Logistic Regression","[{\"question\":\"What problem does this thesis address in cervical cancer research?\",\"answer\":\"It addresses the challenge of building accurate machine learning models to predict treatment outcomes for locally advanced cervical cancer despite open issues like limited data, varying quality, and class imbalance.\"},{\"question\":\"What data sources and preprocessing steps are used?\",\"answer\":\"The study uses a multi-source dataset including medical images, gene scores, and clinical data, with a preprocessing pipeline designed to prepare data for training machine-learning models.\"},{\"question\":\"How are feature selection and data imbalance handled?\",\"answer\":\"Repeated Elastic Net Technique (RENT) is used for feature selection to reduce dimensionality and focus on influential variables, while SMOTE is applied to address class imbalance and evaluate its effect on model performance.\"}]","Treatment Outcome Prediction in Locally Advanced Cervical Cancer - A Machine Learning Approach using Feature Selection on Multi-Source Data | PDF",1785818094,290,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"treatment-outcome-prediction-in-locally-advanced-cervical-cancer-a-machine-learning-approach-using-feature-selection-on-multi-source-data","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/treatment-outcome-prediction-in-locally-advanced-cervical-cancer-a-machine-learning-approach-using-feature-selection-on-multi-source-data/123701/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this thesis address in cervical cancer research?","Question",{"text":75,"@type":76},"It addresses the challenge of building accurate machine learning models to predict treatment outcomes for locally advanced cervical cancer despite open issues like limited data, varying quality, and class imbalance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources and preprocessing steps are used?",{"text":80,"@type":76},"The study uses a multi-source dataset including medical images, gene scores, and clinical data, with a preprocessing pipeline designed to prepare data for training machine-learning models.",{"name":82,"@type":73,"acceptedAnswer":83},"How are feature selection and data imbalance handled?",{"text":84,"@type":76},"Repeated Elastic Net Technique (RENT) is used for feature selection to reduce dimensionality and focus on influential variables, while SMOTE is applied to address class imbalance and evaluate its effect on model performance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]