Journal of Data Science and Its Applications
Vol 3 No 2 (2020): Journal of Data Science and Its Applications

Cancer Detection based on Microarray Data Classification Using Principal Component Analysis and Functional Link Neural Network

Iyon Priyono (Telkom University)
Adiwijaya Adiwijaya (Unknown)
Annisa Aditsania (Unknown)

Article Info

Publish Date
30 Jul 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.

Copyrights © 2020

Journal Info





Computer Science & IT Decision Sciences, Operations Research & Management


JDSA welcomes all topics that are relevant to data science, computational linguistics, and information sciences. The listed topics of interest are as follows: Big Data Analytics Computational Linguistics Data Clustering and Classifications Data Mining and Data Analytics Data Visualization ...