ItemConference Paper

Enhancing Prognostication in Colorectal Cancer with Integrated Machine Learning for Improved Survival Prediction

Title
Enhancing Prognostication in Colorectal Cancer with Integrated Machine Learning for Improved Survival Prediction
Creator
Velmurugan, R.; Suresh, K.; Thilagaraj, T.; Sharma, Meenakshi; Saranya, D.
Description
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.
Source
Proceedings of the International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025;pp.1681-1686
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Colorectal cancer; Machine learning; Personalized treatment; Prognostication; Survival outcomes
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833150982-8;
Format
online
Language
English
Type
Conference paper

Citation

Velmurugan, R.; Suresh, K.; Thilagaraj, T.; Sharma, Meenakshi; Saranya, D., “Enhancing Prognostication in Colorectal Cancer with Integrated Machine Learning for Improved Survival Prediction,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26067.

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