[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120220-en":3,"doc-seo-120220-105":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},120220,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",7,"Healthcare","Machine Learning and Genetic Programming to Enhance Diagnosis and Prognosis in Breast Cancer Care - Master Thesis","Breast cancer involves uncontrolled cell multiplication in mammary tissue and remains a leading malignant condition affecting women worldwide. The thesis examines how recent advances in medicine and artificial intelligence can support more reliable clinical decision-making through machine learning and genetic programming models. The research targets uncertainty in diagnosis and prognosis, improving classification of malignant versus benign cytology, recurrence prediction, and time-to-recurrence or disease-free time estimation. Models are trained and evaluated using the Breast Cancer Wisconsin repository with rigorous metric-based assessment.","Master’s Degree Program in  \nData Science and Advanced Analytics  \nMDSAA  \nMachine Learning and Genetic Programming to Enhance Diagnosis and Prognosis in Breast Cancer Care  \nCatarina Natário Moreira  \nMaster Thesis  \npresented as partial requirement for obtaining a Master’s Degree in Data Science and Advanced Analytics  \nNOVA Information Management School Instituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nNOVA Information Management School  \nInstituto Superior de Estatística e Gestão de Informação  \nUniversidade Nova de Lisboa  \nMACHINE LEARNING AND GENETIC PROGRAMMING TO ENHANCE DIAGNOSIS AND PROGNOSIS IN BREAST CANCER CARE  \nby  \nCatarina Natário Moreira  \nMaster Thesis presented as partial requirement for obtaining the Master’s Degree in Data Science and Advanced Analytics, with a specialization in Data Science  \nSupervised by  \nLeonardo Vanneschi, PhD, NOVA Information Management School (NOVA IMS)  \nSTATEMENT OF INTEGRITY  \nI hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Rules of Conduct and Code of Honor from the NOVA Information Management School.  \nLisbon, 2024  \nABSTRACT  \nBreast cancer is a pathological condition characterized by uncontrolled cell multiplication within mammary tissue and is undoubtedly the malignant neoplasm that most affects women worldwide. Nevertheless, comprehending its etiology and its therapeutic strategies has progressively evolved throughout history and, in recent years, significant advancements in medicine and artificial intelligence have bolstered the deployment of machine learning and genetic programming algorithms. The use of these algorithms has not only caused a significant impact but has also demonstrated remarkable efficacy in developing predictive models for precise and expeditious patient disease diagnosis and prognosis.  \nThe process that guided my research began with the identification and definition of the primary problem: the uncertainty in breast cancer diagnosis and prognosis, along with the limited availability of diverse diagnostic and prognostic algorithms for accurate results. This challenge served as the impetus for my thesis. Consequently, my research pursued the objective of applying machine learning and genetic programming techniques to improve the accuracy and efficiency of breast cancer diagnosis and prognosis. In terms of diagnosis, the primary problem to address is distinguishing between malignant and benign breast cytology. Regarding prognosis, my research focused on two main problems: firstly, distinguishing between recurrent and non-recurrent breast cancer, and secondly, predicting the recurrence time for recurrent cases as well as the disease-free time for non-recurrent cases.  \nThe research encompasses analyzing data from the Breast Cancer Wisconsin repository. This includes understanding the data, preprocessing, feature selection, and implementing and training machine learning and genetic programming algorithms to accurately diagnose and prognose cancer. Subsequently, the performance of the developed models was rigorously evaluated using different metrics.  \nUpon result analysis, Machine Learning and Genetic Programming algorithms significantly enhance Breast Cancer diagnosis, with strong performances from Multi-Layer Perceptron, Voting Classifier, and Stacking Classifier. However, prognosis outcomes were generally suboptimal, except for the top performance of the Voting Classifier and promising results from TPOT in classification. The Gradient Boosting Regressor consistently excelled in regression tasks.  \nIn conclusion, the development of the research methodology culminated in the successful implementation of machine learning and genetic programming models for breast cancer diagnosis and progno","cbCaimFjo8N8zQOn","https://ap.wps.com/l/cbCaimFjo8N8zQOn","pdf",8738309,1,216,"English","en",105,"# 1. INTRODUCTION\n## 1.1. CONTEXT, PROBLEM, AND MOTIVATION\n## 1.2. OBJECTIVES\n## 1.3. 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