Abstract
Cancer is characterized by tremendous increase in the number of cells which can affect the organs of body. Thanks to the rapid technological advances of recent years, it is now possible to analyze the cellular structure of different types of cancer in a short period of time. DNA microarrays printed microscope slides have thousands of tiny dots in the specified location, each containing a sequence of DNA or a gene. The objective of this paper is to use Particle Swarm Optimization as a transform for increasing the machine learning algorithms such as Quadratic Discriminant Analysis, K Nearest Neighbor Classifier, Random Forest Classifier. Gene expression data related to 358 Liver cancer and 290 Breast cancer subjects are considered in this analysis. Principal Component Analysis used for reducing the dimensionality of data. Notably, Quadratic Discriminant Analysis when used with Particle Swarm Optimization provides the highest Balanced Accuracy score of 96 % for liver cancer data while K Nearest Neighbor classifier along with Particle Swarm Optimization provides the highest Balanced Accuracy score of 91 % for breast cancer data.
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