Abstract
Integrating multiple omics data can significantly improve the accuracy of cancer subclassification, a challenging task due to the high dimensionality and limited sample sizes. The integration of these data sets can enhance model performance. This study addresses these challenges by employing Quantum Cat Swarm Optimization (QCSO) for feature selection, along with K-means clustering and Support Vector Machine (SVM) for classification. Using QCSO, the most significant features were identified, resulting in an increase in accuracy from 81% to100%. Performance was evaluated using accuracy, F1-score, precision, recall, ROC, and the silhouette metric, all of which confirmed the effectiveness of the feature selection approach. Additionally, this method enhances the classification of samples whilemaking the models more interpretable, providing better insights into the molecular mechanisms of cancer. This work contributes to advancing knowledge in the field of cancer research and biology in general
Recommended Citation
Mohammed, Mazin Abed and Ali, Ali Mahmoud
(2024)
"Enhanced Cancer Subclassification Using Multi-Omics Clustering and Quantum Cat Swarm Optimization,"
Iraqi Journal for Computer Science and Mathematics: Vol. 5:
Iss.
3, Article 37.
DOI: https://doi.org/10.52866/ijcsm.2024.05.03.035
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol5/iss3/37
