[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119409-en":3,"doc-seo-119409-105":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":20,"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},119409,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Enhancing Breast Cancer Detection with Machine Learning - A Predictive Modeling Approach","Compared to conventional diagnostic methods, breast cancer detection can lack sufficient sensitivity and specificity. Advances in machine learning enable risk assessment and outcome prediction, supporting personalized data-driven decision-making for screening and treatment planning. This research evaluates how algorithm-generated data can identify and categorize breast cancer effectively using enhanced feature selection, reliable classifiers, and tuned model training. Results show Naive Bayes and Random Forest achieving top performance, with ROC and accuracy metrics indicating strong predictive capability.","Enhancing Breast Cancer Detection with Machine Learning: A Predictive Modeling Approach  \nSEEJPH Volume XXVI, 2025, ISSN: 2197-5248; Posted:04-01-2025  \nEnhancing Breast Cancer Detection with Machine Learning: A Predictive Modeling Approach  \nJ. Sherine Glory1*, M. Bhavani2, B. Saratha3, A. Akila4, Dr. B. Prathusha Laxmi5,  \nDr. V. Vijayaraja6, N. Raghavendran7  \n1*Assistant Professor, Department of Computer Science and Engineering, R.M.D. Engineering College, Kavaraipettai, Chennai  \n2Assistant Professor, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai  \n3Assistant Professor, Department of Artificial Intelligence and Data Science, R.M.K. Engineering College, Chennai 4Assistant Professor, Department of Artificial Intelligence and Data Science, R.M.K College of Engineering and Technology, Chennai  \n5Professor, Department of Artificial Intelligence and Data Science, R.M.K. College of Engineering and Technology, Chennai  \n6Professor, Department of Artificial Intelligence and Data Science, R.M.K. College of Engineering and Technology, Chennai  \n7Assistant Professor, Department of Artificial Intelligence and Data Science, R.M.K College of Engineering and Technology. Chennai  \n[sherinegloryj@gmail.com](sherinegloryj@gmail.com1)[1](sherinegloryj@gmail.com1)*, [mbhavani1811@gmail.com](mbhavani1811@gmail.com2)[2](mbhavani1811@gmail.com2), [bsa.ad@rmkec.ac.in](bsa.ad@rmkec.ac.in3)[3](bsa.ad@rmkec.ac.in3), [aakilaads@rmkcet.ac.in](aakilaads@rmkcet.ac.in4)[4](aakilaads@rmkcet.ac.in4),  \n[hod_ads@rmkcet.ac.in](hod_ads@rmkcet.ac.in5)[5](hod_ads@rmkcet.ac.in5), [vijayarajaads@rmkcet.ac.in](vijayarajaads@rmkcet.ac.in6)[6](vijayarajaads@rmkcet.ac.in6), [ragavendrannv2001@gmail.com](ragavendrannv2001@gmail.com7)[7](ragavendrannv2001@gmail.com7)  \nKEYWORDS  \nBreast Cancer; Machine Learning, feature selection, Naive Bayes and Random Forest  \nABSTRACT  \nCompared to other methods, the one now used to diagnose breast cancer is not as sensitive or specific. The development of increasingly accurate machine learning algorithms for risk assessment, prediction, and treatment planning has made personalized breast cancer data a reality. The effectiveness of data produced by machine learning algorithms in identifying and categorizing breast cancer is examined in this research. In this post, we will go over a machine-learning strategy that may improve breast cancer diagnosis. To improve the speed and accuracy of diagnoses, our approach utilizes enhanced feature selection methods, reliable classification algorithms, and top-notch model training. After the models were built, we put in a lot of time and effort with the hyperparameters to evaluate various ML approaches. A ROC score of 1.00 for Naive Bayes and a score of 98.10% for Random Forest were the two best models. This study proves that ML algorithms, includingthe Naive Bayes and random forest methods, can accurately forecast breast cancer outcomes. Machine learning might be used to assess the situation in the future.  \n.  \nEnhancing Breast Cancer Detection with Machine Learning: A Predictive Modeling Approach  \nSEEJPH Volume XXVI, 2025, ISSN: 2197-5248; Posted:04-01-2025  \n1. Introduction:  \nThe second-biggest killer of women is breast cancer, followed by heart disease. Additionally, it affects about 10% of females. World Health Organisation data shows that around 500,000 women get a breast cancer diagnosis every year [1] . Women in impoverished countries sometimes reach an advanced stage with few treatment options since screening programs and awareness are lacking. The likelihood of breast cancer developing in a woman may be increased by certain factors. Her reproductive history (including nulliparity, early menarche, or late menopause), hormonal variables (including oral contraceptives or hormone replacement treatment), obesity, smoking, heavy alcohol use, or early radiation exposure are all potential causes [2] . Cancer affected a large number of people during t","cbCailB1RGWiQtM9","https://ap.wps.com/l/cbCailB1RGWiQtM9","pdf",419429,1,10,"English","en",105,"# 1. Introduction\n## Breast cancer burden and risk factors\n## Diagnostic challenges and clinical assessment criteria\n## Role of AI and machine learning in prediction\n## Medical imaging data and feature extraction","[{\"question\":\"Why is breast cancer diagnosis challenging with traditional methods?\",\"answer\":\"Conventional approaches are not sufficiently sensitive or specific, which limits reliable identification and classification of benign versus malignant cases.\"},{\"question\":\"What approach does the study propose to improve detection using machine learning?\",\"answer\":\"The study builds predictive models using enhanced feature selection, dependable classification algorithms, and extensive hyperparameter tuning during training.\"},{\"question\":\"Which models performed best and what metrics were reported?\",\"answer\":\"Naive Bayes achieved an ROC score of 1.00, while Random Forest reached 98.10% accuracy, indicating strong predictive effectiveness.\"}]","Enhancing Breast Cancer Detection with Machine Learning - 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