Enhanced Cancer Subclassification Using Multi-Omics Clustering and Quantum Cat Swarm Optimization

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% to 100%. 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 while making 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.