[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125030-en":3,"doc-seo-125030-105":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},125030,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning techniques in breast cancer preventive diagnosis - a review","Breast cancer is the most prevalent cancer among women, and recent studies show that machine learning can predict five-year breast cancer risk using personal health data. This review examines how dataset size, feature selection, and model choice shape analytical performance across common classifiers such as SVM, Random Forest, K-NN, Naive Bayes, neural networks, decision trees, logistic regression, and discriminant analysis. A set of 54 selected papers was analyzed after screening 3917 records from Scopus and PubMed.","Machine learning techniques in breast cancer preventive diagnosis: a review  \nGiada Anastasi1,4 · Michela Franchini1 · Stefania Pieroni1 · Marina Buzzi2 · Maria Claudia Buzzi2 · Barbara Leporini3 · Sabrina Molinaro1  \nReceived: 20 June 2023 / Revised: 7 October 2023 / Accepted: 25 February 2024 /  \nPublished online: 12 March 2024 © The Author(s) 2024  \nAbstract  \nBreast cancer (BC) is known as the most prevalent form of cancer among women. Recent research has demonstrated the potential of Machine Learning (ML) techniques in predicting the five-year BC risk using personal health data. Support Vector Machine (SVM), Random Forest, K-NN (K-Nearest Neighbour), Naive Bayes, Neural Network, Decision Tree (DT), Logistic Regression (LR), Discriminant Analysis, and their variants are commonly employed in ML for BC analysis. This study investigates the factors influencing the performance of ML techniques in the domain of BC prevention, with a focus on dataset size and feature selection. The study’s goal is to examine the effect of dataset cardinality, featureselection, and model selection on analytical performance in terms of Accuracy and Area Under the Curve (AUC) . To this aim, 3917 papers were automatically selected from Scopus and PubMed, considering all publications from the previous 5 years, and, after inclusion and exclusion criteria, 54 articles were selected for the analysis. Our findings highlight how a good cardinality of the dataset and effective feature selection have a higher impact on the model’s performance than the selected model, as corroborated by one of the studies, which gets extremely good results with all of the models employed.  \nKeywords Breast cancer · Machine learning · Preventive diagnosis · Random forest · Support vector machine  \nAbbreviations  \nFFDM  \nABUS  \nCT  \nUWB  \nH&E  \nMRIDCE-MRImpMRI ADH  \nUS  \nQUS  \nFull-Field digital mammography Automated breast ultrasound screening Computed tomography  \nUltra-WideBand  \nHematoxylin and eosin  \nMagnetic resonance imaging Dynamic contrast-enhanced MRI MultiParametric MRI  \nAtypical ductal hyperplasia UltraSound  \nQuantitative US  \nExtended author information available on the last page of the article  \nqCT Quantitative CT  \nFFPE Formalin-fixed paraffin-embedded  \nBCIMS Breast cancer information management system CEDM Contrast-enhanced digital mammography WDBC Wisconsin diagnostic breast cancer  \nCBC Coimbra breast cancer  \n1 Introduction  \n1.1 Historical review  \nCancer is recognized as a significant healthcare challenge by the Horizon Europe program [1] . Among female cancers, Breast Cancer (BC) is the most prevalent, with an incidence rate of 5 cases per 1,000 women, as extensively documented in the literature [2–8] . In the European Union (EU) in 2020, 2.7 million BC cases were diagnosed, resulting in 1.3 million deaths. The World Health Organization (WHO) guidelines strongly recommend optimising cancer treatment and care [9] . The \"European Commission Cancer Plan\" highlights the crucial role of cancer prevention and treatment optimization [10] . It also provides information on the allocation of funds for cancer research on early detection and introduces anew \"EU supported Cancer Screening Scheme\" aiming to offer screening to 90% of the EU population by 2025. As an immediate objective, the European Commission plans to propose an update to the Council Recommendation on cancer screening by 2022, incorporating the most recent scientific evidence. The updated recommendation suggests expanding cancer screening campaigns beyond breast, colorectal, and cervical cancer to include prostate, lung, and gastric cancer. Furthermore, the Commission proposes identifying criteria to target screening based on personal risk and characteristics rather than just age.  \nBC is categorised into three subtypes based on the presence or absence of molecular markers for estrogen receptor (ER) or progesterone receptor (PR) and human epidermal growth factor 2 (ERBB2 or HER2) . Specifically, h","cbCaiiB2sYSxUiWX","https://ap.wps.com/l/cbCaiiB2sYSxUiWX","pdf",3239424,1,44,"English","en",105,"# Abstract\n# Abbreviations\n# Introduction\n## Historical review","[{\"question\":\"Which machine learning models are discussed for breast cancer preventive diagnosis?\",\"answer\":\"The review highlights SVM, Random Forest, K-NN, Naive Bayes, neural networks, decision trees, logistic regression, discriminant analysis, and their variants.\"},{\"question\":\"What factors are evaluated as drivers of model performance in breast cancer prevention?\",\"answer\":\"Dataset cardinality (dataset size), feature selection, and model selection are investigated for their impact on accuracy and AUC.\"},{\"question\":\"How many papers were screened and finally selected for the analysis?\",\"answer\":\"3917 papers were automatically selected from Scopus and PubMed, and 54 articles were ultimately selected after inclusion and exclusion criteria.\"}]","Machine learning techniques in breast cancer preventive diagnosis - 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