Open AccessDiscrete Wavelet Transform (DWT) and Random Forest for Cancer Detection Based on Microarray Data Classification
Author(s) -
Monica Triyani,
Adiwijaya Adiwijaya,
Annisa Aditsania
Publication year - 2020
Publication title -
jurnal infotel/jurnal infotel
Language(s) - English
Resource type - Journals
eISSN - 2460-0997
pISSN - 2085-3688
DOI - 10.20895/infotel.v12i3.484
Subject(s) - discrete wavelet transform , random forest , cancer , dimension (graph theory) , pattern recognition (psychology) , data mining , wavelet transform , computer science , artificial intelligence , wavelet , mathematics , algorithm , medicine , pure mathematics
Cancer is one of the leading causes of death worldwide. According to the World Health Organization (WHO), in 2018, about 9.6 million deaths caused by cancer. DNA microarray technology has played an important role in analyzing and diagnosing cancer. The accuracy resulting from the classification of Random Forests is not optimal because microarrays have large dimensional data. Therefore, it is necessary to reduce the dimensions of the Discrete Wavelet Transform (DWT) as a feature to reduce dimensions and increase accuracy in microarray data. Based on the simulation, the dimension can be reduced and improve the accuracy of classification up to 8% - 20%. DWT approximation coefficient can improve accuracy better than detailed coefficients for data on colon cancer 100%, lung cancer 100%, ovarian 100%, prostate tumor 80%, and central nervous system 83.33%.