Enhancing Cancer Classification in Genomic Data: A Hybrid Approach Using Principal Component Analysis and Machine Learning Techniques

Using the abundance of genetic data available, cancer categorization using genomic data is an important field of study aimed at improving the diagnosis, prognosis, and treatment of different forms of cancer. Genomic data offers a full picture of the molecular mechanisms behind cancer. This data includes gene expression levels, DNA methylation patterns, copy number variations, and mutations. However, reliable cancer classification is severely hampered by the enormous dimensionality and complexity of these datasets. It is suggested that in order to improve the performance and interpretability of the classification model in cancer data, this hybrid technique should make use of both SVM and other traditional PCA. In order to improve performance while maintaining biological relevance in genomic characteristics, the approach entails employing PCA to reduce the dimensions. After that, the reduced dataset's complicated patterns are found using the SVM model. The strategy is put to the test to see how well it handles high-dimensional genetic data and what potential it creates for improving cancer detection and customized treatment regimens.