ItemConference Paper

Comparative Analysis of Machine Learning Models for Uterine Cancer Prediction Using Clinical and Genomic Data

Title
Comparative Analysis of Machine Learning Models for Uterine Cancer Prediction Using Clinical and Genomic Data
Creator
Nwigwe, Simon; Iwendi, Celestine; Sharma, Vandana; Ojo, Olayinka Anthony; Uwah, Salome Enoshi; Nwibo, Ezekiel Gabriel
Description
Uterine cancer prediction accuracy is important in clinical decision-making because it improves the overall chances of patient recovery. Several machine learning models, such as Decision Tree, Random Forest, XGBoost Regressor, and Support Vector Regressor, were explored to determine which is more effective in predicting uterine cancer. Attributes such as mutation counts, diagnosis age, and MSI score, were used for the analysis. The different models were tested using the standard performance metrics such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 Score. Random Forest showed the highest predictive performance with an R2 score of 0.655, followed by XGBoost regressor, which was relatively close to the R2 score of Random Forest. Support Vector Regressor performed very poorly as the R2 score was negative, implying that the model is not suitable for such prediction. Ensemble-based models, which include Random Forest and XGBoost Regressor, have proven to be more effective in handling medical prediction tasks, and this is because of their robustness and their ability when it comes to handle overfitting. Though model generalizability was affected due to small data size and the absence of hyperparameter tuning. The future work will focus on expanding the dataset, implementing hyperparameter tuning, integrating deep learning, and leveraging explainable AI (XAI). The research has provided valuable insight for clinicians who wish to use machine learning for uterine cancer prognosis. 2025 IEEE.
Source
2025 12th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Clinical Data; Genomic Data; Machine Learning; Medical AI; Prediction; Random Forest; Uterine Cancer; XGBoost
Coverage
Nwigwe S., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Computer Science Department, Bengaluru, India; Ojo O.A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Uwah S.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Nwibo E.G., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155421-7;
Format
online
Language
English
Type
Conference paper

Citation

Nwigwe, Simon; Iwendi, Celestine; Sharma, Vandana; Ojo, Olayinka Anthony; Uwah, Salome Enoshi; Nwibo, Ezekiel Gabriel, “Comparative Analysis of Machine Learning Models for Uterine Cancer Prediction Using Clinical and Genomic Data,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26101.

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