Light Gradient Boosting with Hyper Parameter Tuning Optimization for COVID-19 Prediction

The 2019 coronavirus disease (COVID-19) caused pandemic and a huge number of deaths in the world. COVID-19 screening is needed to identify suspected positive COVID-19 or not and it can reduce the spread of COVID-19. The polymerase chain reaction (PCR) test for COVID-19 is a test that analyzes the respiratory specimen. The blood test also can be used to show people who have been infected with SARS-CoV-2. In addition, age parameters also contribute to the susceptibility of COVID-19 transmission. This paper presents the extra trees classification with random over-sampling by considering blood and age parameters for COVID-19 screening. This research proposes enhanced preprocessing data by using KNN Imputer to handle large missing values. The experiments evaluated the existing classification methods such as Random Forest, Extra Trees, Ada Boost, Gradient Boosting, and the proposed Light Gradient Boosting with hyperparameter tuning to measure the predictions of patients infected with SARS-CoV-2. The experiments used Albert Einstein Hospital test data in Brazil that consisted of 5,644 sample data from 559 patients with infected SARS-CoV-2. The experimental results show that the proposed scheme achieves an accuracy of about 98,58%, recall of 98,58%, the precision of 98,61%, F1-Score of 98,61%, and AUC of 0,9682.

@article{Ernawan2022,
  title     = {Light Gradient Boosting with Hyper Parameter Tuning Optimization for COVID-19 Prediction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {8},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Ferda Ernawan and Kartika Handayani and Mohammad Fakhreldin and Yagoub Abbker},
  doi       = {10.14569/IJACSA.2022.0130859},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130859}
}