[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119783-en":3,"doc-seo-119783-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119783,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Predicting Environment Effects on Breast Cancer by Implementing Machine Learning","Breast cancer increasingly contributes to female mortality, with evidence that environmental factors materially influence its occurrence and progression alongside genetic drivers. This study reviews environmental determinants of breast cancer risk, incidence, and outcomes, focusing on lifestyle-related hormonal imbalance and inflammation, as well as contaminants such as pesticides, endocrine-disrupting chemicals, and industrial emissions that can affect hormone signaling and DNA integrity. Machine learning classifiers are applied for predictive modeling, with performance assessed using confusion matrix-derived metrics and ROC analysis, achieving strong accuracy with Random Forest.","Predicting environment effects on breast cancer by implementing machine learning  \nMuhammad Shoaib Farooq, Mehreen Ilyas  \nDepartment of Computer Science, School of System and Technology, University of Management and Technology, Lahore, 54000 Corresponding author: Muhammad Shoaib Farooq ([Shoaib.farooq@umt.edu.pk](Shoaib.farooq@umt.edu.pk))  \nABSTRACT The biggest Breast cancer is increasingly a major factor in female fatalities, overtaking heart disease. While genetic factors are important in the growth of breast cancer, new research indicates that environmental factors also play a substantial role in its occurrence and progression. The literature on the various environmental factors that may affect breast cancer risk, incidence, and outcomes is thoroughly reviewed in this study report. The study starts by looking at how lifestyle decisions, such as eating habits, exercise routines, and alcohol consumption, may affect hormonal imbalances and inflammation, two important factors driving the development of breast cancer. Additionally, it explores the part played by environmental contaminants such pesticides, endocrine-disrupting chemicals (EDCs), and industrial emissions, all of which have been linked to a higher risk of developing breast cancer due to their interference with hormone signaling and DNA damage. Algorithms for machine learning are used to express predictions. Logistic Regression, Random Forest, KNN Algorithm, SVC and extra tree classifier. Metrics including the confusion matrix correlation coefficient, F1-score, Precision, Recall, and ROC curve were used to evaluate the models. The best accuracy among all the classifiers is Random Forest with 0.91% accuracy and ROC curve 0.901% of Logistic Regression. The accuracy of the multiple algorithms for machine learning utilized in this research was good, which is important and indicates that these techniques could serve as replacement forecasting techniques in breast cancer survival analysis, notably in the Asia region.  \nKEY WORDS: Pollution and Breast Cancer, Disease Prediction, SVC, Chemical Toxicants, Machine learning Models, Breast Cancer prediction  \nI. INTRODUCTION  \nThe irregular cell development known as a tumor frequently spreads to other body parts. Knowing how and at what stage cancer formed in this patient is important since there are many different varieties of cancer, each of which is divided into numerous classes and categories [1] . Humans are now more susceptible than ever to developing several types of cancer. One out of every six fatalities is regarded as due to cancer, which is a primary reason for death globally. The most widespread cancer is breast cancer in terms of new cases. Over 40,920 women died in 2018 from breast cancer alone. The World Health Organization (WHO) estimates that 2.90 million women worldwide receive a breast cancer diagnosis each year. More than 100 diseases that affect various parts of the human body are referred to as cancer [2] . While it is well-known that genetic variables can increase awoman's risk of developing breast cancer, recent studies have shown how important environmental factors are to the progression of the condition. Numerous environmental factors, such as exposure to air pollution, endocrinedisrupting chemicals, socioeconomic status, and geographic location, affect the chance of developing breast cancer. In the early stages of cancer, there are a variety of unique approaches that scientists have discovered to predict the efficacy of therapy. The advancement of medicine and healthcare technology has led to a plethora of data about this matter. Here, we propose a machine-learning approach centered round patient data that was previously gathered from a large number of patients. In recent years, there has been a surge when applying machine learning techniques in the healthcare area as a means of making accurate diagnoses  \nand classifying patients' conditions. More recently, Strategies for machine learning have been ","cbCaimAcSB8vGTQF","https://ap.wps.com/l/cbCaimAcSB8vGTQF","pdf",758705,1,"English","en",105,"# Introduction\n## Breast cancer burden and significance\n## Environmental factors and machine learning motivation\n# Methods\n## Environmental factors considered\n## Machine learning classifiers used\n## Evaluation metrics\n# Results\n## Model performance comparison","[{\"question\":\"Which environmental factors are examined for breast cancer prediction?\",\"answer\":\"The study considers lifestyle-related factors (eating habits, exercise routines, alcohol consumption) affecting hormonal imbalance and inflammation, and environmental contaminants such as pesticides, endocrine-disrupting chemicals, and industrial emissions.\"},{\"question\":\"How is machine learning used in this research?\",\"answer\":\"Machine learning classification algorithms generate predictions using patient-related data combined with environmental factors, including Logistic Regression, Random Forest, KNN, SVC, and Extra Trees.\"},{\"question\":\"What metrics evaluate the predictive models?\",\"answer\":\"Evaluation uses confusion matrix-derived measures and related metrics including correlation coefficient, F1-score, Precision, Recall, and the ROC curve.\"}]","Predicting Environment Effects on Breast Cancer by Implementing Machine Learning | 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environmental factors are examined for breast cancer prediction?","Question",{"text":74,"@type":75},"The study considers lifestyle-related factors (eating habits, exercise routines, alcohol consumption) affecting hormonal imbalance and inflammation, and environmental contaminants such as pesticides, endocrine-disrupting chemicals, and industrial emissions.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How is machine learning used in this research?",{"text":79,"@type":75},"Machine learning classification algorithms generate predictions using patient-related data combined with environmental factors, including Logistic Regression, Random Forest, KNN, SVC, and Extra Trees.",{"name":81,"@type":72,"acceptedAnswer":82},"What metrics evaluate the predictive models?",{"text":83,"@type":75},"Evaluation uses confusion matrix-derived measures and related metrics including correlation coefficient, F1-score, Precision, Recall, and the ROC 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