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

Comparative Analysis of Support Vector Machine-Recursive Feature Elimination and Chi-Square on Microarray Classification for Cancer Detection with Naïve Bayes

Talitha Kayla Amory (Universitas Telkom)
Adiwijaya Adiwijaya (Universitas Telkom)
Widi Astuti (Universitas Telkom)

Article Info

Publish Date
30 Jul 2020


Cancer is a world-famous deadly disease. According to the World Health Organization (WHO), cancer is the second leading cause of death globally and is responsible for an estimated 9.6 million deaths in 2018. One well-known technique for cancer detection is the DNA microarray technique. DNA microarray technology provides an opportunity for researchers to analyze thousands of gene expression profiles at the same time to determine whether a person has cancer or not. However, one of the problems in DNA microarray data is the large number of features that require feature selection. In overcoming these problems, this study will use the feature selection Support Vector Machine-Recursive Feature Elimination (SVM-RFE) and Chi-Square and use the Naïve Bayes classification method. The accuracy results from using feature selection with those that are not will be compared. The accuracy between using the two feature selection methods will also be compared to find which feature selection method is better when combined with the Naïve Bayes classification method. To get an overall picture of the performance comparison, this study also considers precision, recall, and F1-score. The best accuracy results obtained were 100% lung cancer data with SVM-RFE and Chi-Square, 99.6% ovarian cancer with SVM-RFE, 93.7% breast cancer with SVM-RFE, and 90% colon cancer with SVM- RFE.

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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 ...