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 Velmurugan, R.; Suresh, K.; Thilagaraj, T.; Sharma, Meenakshi; Saranya, D. Title A name given to the resource Enhancing Prognostication in Colorectal Cancer with Integrated Machine Learning for Improved Survival Prediction 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 Proceedings of the International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025;pp.1681-1686 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICMSCI62561.2025.10894577" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICMSCI62561.2025.10894577</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105000463509?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105000463509?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 Velmurugan R., Kristu Jayanti College, Autonomous, Department of Computer Science (PG), Bangalore, India; Suresh K., Christ (Deemed to be University), Department of Computer Science, Bangalore, India; Thilagaraj T., Moodlakatte Institute of Technology, Department of Artificial Intelligence and Machine Learning, Karnataka, Kundapura, India; Sharma M., Global Group of Institutes, Punjab, Amritsar, India; Saranya D., Sri Eshwar College of Engineering, Department of Information Technology, Coimbatore, India Description An account of the resource Machine learning methods are recently used to predict patient survival in colorectal cancer using such models as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), VGG16, and Support Vector Machines (SVM). Taking advantage of a combination of CT, MRI scan images, and clinical records with drug recommendations, the study also checks to see how these models compare for distinguishing between patients in terms of their illness course-whether they are going to get better or worse over time. The results reveal VGG16 has better accuracy than CNN, RNN and SVM; as the highest-performing model tested, it also demonstrates superior precision, recall and F1-score. The research findings also validate these proposed models as they compare favorably with existing literature. This presents a promising proposition: a new, revolutionary approach to using artificial intelligence to boost prognostic accuracy. 2025 IEEE. Subject The topic of the resource Colorectal cancer; Machine learning; Personalized treatment; Prognostication; Survival outcomes Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833150982-8; 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