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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. However, microarray data has a large data dimensions resulting in the accuracy of the Random Forest are not optimal. In this paper, the Discrete Wavelet Transform (DWT) is selected as a feature extraction method. 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 85.71%, and central nervous system 83.33%.
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