Comparison of Supervised Learning Methods for COVID-19 Classification on Chest X-Ray Image

Authors

  • Faisal Dharma Adhinata Institut Teknologi Telkom Purwokerto
  • Nur Ghaniaviyanto Ramadhan Institut Teknologi Telkom Purwokerto
  • Arif Amrulloh Institut Teknologi Telkom Purwokerto
  • Arief Rais Bahtiar Institut Teknologi Telkom Purwokerto

DOI:

https://doi.org/10.21512/commit.v16i2.7970

Keywords:

Supervised Learning Methods, COVID- 19 Classification, Chest X-Ray Images

Abstract

The Coronavirus (COVID-19) pandemic is still ongoing in almost all countries in the world. The spread of the virus is very fast because the transmission process is through air contaminated with viruses from COVID-19 patients’ droplets. Several previous studies have suggested that the use of chest X-Ray images can detect the presence of this virus. Detection of COVID-19 using chest X-Ray images can use deep learning techniques, but it has the disadvantage that the training process takes too long. Therefore, the research uses machine learning techniques hoping that the accuracy results are not too different from deep learning and result in fast training time. The research evaluates three supervised learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Random Forest, to detect COVID-19. The experimental results show that the accuracy of the SVM method using a polynomial kernel can reach 90% accuracy, and the training time is only 462 ms. Through these results, machine learning techniques can compensate for the results of the deep learning technique in terms of accuracy, and the training process is faster than the deep learning technique. The research provides insight into the early detection of COVID-19 patients through chest X-Ray images so that further medical treatment can be carried out immediately.

Dimensions

Plum Analytics

Author Biographies

Faisal Dharma Adhinata, Institut Teknologi Telkom Purwokerto

Department of Software Engineering, Faculty of Informatics

Nur Ghaniaviyanto Ramadhan, Institut Teknologi Telkom Purwokerto

Department of Software Engineering, Faculty of Informatics

Arif Amrulloh, Institut Teknologi Telkom Purwokerto

Department of Software Engineering, Faculty of Informatics

Arief Rais Bahtiar, Institut Teknologi Telkom Purwokerto

Department of Software Engineering, Faculty of Informatics

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Published

2022-06-08
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