Cancer Detection based on Microarray Data Classification Using Principal Component Analysis and Functional Link Neural Network - Iyon Priyono; Adiwijaya Adiwijaya; Annisa Aditsania (Telkom University, 2020)
Cancer is a deadly disease caused by abnormal growth of tissue cells that are not controlled in the body. In 2018, according to Globocan data, the number of cancer sufferers has increased from the previous years which was 18.1 million people, with a mortality rate of 9.6 million. In recent years, cancer prediction using DNA microarrays data can help medical experts in analyzing whether a person has cancer or not. DNA microarray data have very large and complex gene expression, therefore a dimensional reduction method is needed. Then, the dimension reduction results will be used for classification into types of cancer or not. In this paper, Principal Component Analysis (PCA) is used as a feature extraction to reduce dimension and Functional Link Neural Network as a classifier. Based on the simulation, the average of accuracy using the FLNN and PCA about 76.08%. Keywords: cancer detection, Microarray data, Functional Link Neural Network, Principal Component Analysis.