Abstract:
This study delves into the critical role of feature selection in enhancing the accuracy of microarray data classification,
particularly in the context of ovarian cancer detection. By harnessing the power of machine learning techniques and microarray
technology, the research endeavors to identify subtle gene expression patterns that serve as indicators of ovarian cancer. By leveraging
machine learning techniques and microarray technology, subtle gene expression patterns indicative of ovarian cancer can be identified.
The research explores the utilization of Principal Component Analysis (PCA) for dimensionality reduction and compares the
effectiveness of feature selection techniques such as Artificial Bee Colony (ABC) and Sequential Forward Floating Selection (SFFS).
The dataset used in this study comprises of 15154 genes, 253 instances, and 2 classes related to ovarian cancer. Through a
comprehensive analysis, the study aims to optimize the classification process and improve the early detection of ovarian cancer.
Moreover, the study presents the classification accuracy results obtained by PCA, ABC, and SFFS. While PCA achieved an accuracy
of 96% and SFFS yielded a classification accuracy of 98%, ABC demonstrated the highest classification accuracy of 100%. These
findings underscore the effectiveness of ABC as the preferred choice for feature selection in improving the classification accuracy of
ovarian cancer detection using microarray data.
