Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Manjunatha Swamy, C.; Babu Kumar, S.; Kiran, B.; Vijay Kumar, M.S. Title A name given to the resource Automation of Breast Cancer Diagnosis and Treatment Using Machine Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1371 LNNS;pp.473-484 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-5723-0_35" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-5723-0_35</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105018915958?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105018915958?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Manjunatha Swamy C., Christ University, Bangalore, India; Babu Kumar S., Christ University, Bangalore, India; Kiran B., PES University, Bangalore, India; Vijay Kumar M.S., MIT, Mysore, India Description An account of the resource Breast cancer remains a major global health challenge, with the complexity of managing diverse diagnostic tests often hindering timely and accurate detection. This system proposes a solution by unifying various test results, such as imaging, biopsy and genetic data, into a single platform that leverages machine learning (ML) to predict the likelihood of breast cancer. The platform features an intuitive dashboard that visually represents deviations from normal values, enabling healthcare providers to make informed decisions for early detection and treatment planning. In addition, the system includes an interactive chatbot powered by natural language processing, which assists both doctors and patients by interpreting test results, explaining predictions and offering real-time suggestions for treatment options. This comprehensive approach not only integrates ML models to enhance diagnostic accuracy but also provides real-time updates and alerts for critical changes in patient data. By consolidating fragmented information and incorporating predictive analytics, the system aims to improve the precision of cancer monitoring and offer personalized treatment guidance. About 98% early detection accuracy is achieved to do decision-making processes better which leads to efficient treatment planning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Breast cancer; Cancer prediction model; Convolutional neural networks (CNN); Machine learning; Machine learning in healthcare; Medical image analysis Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981965722-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online