HGSOXGB: Hunger-Games-Search-Optimization-Based Framework to Predict the Need for ICU Admission for COVID-19 Patients Using eXtreme Gradient Boosting
Article
Pinki, Farhana Tazmim, Awal, Md Abdul, Mumenin, Khondoker Mirazu, Hossain, Md. Shahadat, Faysal, Jabed Al, Rana, Rajib, Almuqren, Rajib, Ksibi, Amel and Samad, Md Abdus. 2023. "HGSOXGB: Hunger-Games-Search-Optimization-Based Framework to Predict the Need for ICU Admission for COVID-19 Patients Using eXtreme Gradient Boosting." Mathematics. 11 (18). https://doi.org/10.3390/math11183960
| Article Title | HGSOXGB: Hunger-Games-Search-Optimization-Based Framework to Predict the Need for ICU Admission for COVID-19 Patients Using eXtreme Gradient Boosting |
|---|---|
| ERA Journal ID | 213646 |
| Article Category | Article |
| Authors | Pinki, Farhana Tazmim, Awal, Md Abdul, Mumenin, Khondoker Mirazu, Hossain, Md. Shahadat, Faysal, Jabed Al, Rana, Rajib, Almuqren, Rajib, Ksibi, Amel and Samad, Md Abdus |
| Journal Title | Mathematics |
| Journal Citation | 11 (18) |
| Article Number | 3960 |
| Number of Pages | 19 |
| Year | 2023 |
| Publisher | MDPI AG |
| ISSN | 2227-7390 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/math11183960 |
| Web Address (URL) | https://www.mdpi.com/2227-7390/11/18/3960 |
| Abstract | Millions of people died in the COVID-19 pandemic, which pressured hospitals and healthcare workers into keeping up with the speed and intensity of the outbreak, resulting in a scarcity of ICU beds for COVID-19 patients. Therefore, researchers have developed machine learning (ML) algorithms to assist in identifying patients at increased risk of requiring an ICU bed. However, many of these studies used state-of-the-art ML algorithms with arbitrary or default hyperparameters to control the learning process. Hyperparameter optimization is essential in enhancing the classification effectiveness and ensuring the optimal use of ML algorithms. Therefore, this study utilized an improved Hunger Games Search Optimization (HGSO) algorithm coupled with a robust extreme gradient boosting (XGB) classifier to predict a COVID-19 patient’s need for ICU transfer. To further mitigate the random initialization inherent in HGSO and facilitate an efficient convergence toward optimal solutions, the Metropolis–Hastings (MH) method is proposed for integration with HGSO. In addition, population diversity was reintroduced to effectively escape local optima. To evaluate the efficacy of the MH-based HGSO algorithm, the proposed method was compared with the original HGSO algorithm using the Congress on Evolutionary Computation benchmark function. The analysis revealed that the proposed algorithm converges better than the original method and exhibits statistical significance. Consequently, the proposed algorithm optimizes the XGB hyperparameters to further predict the need for ICU transfer for COVID-19 patients. Various evaluation metrics, including the receiver operating curve (ROC), precision–recall curve, bootstrap ROC, and recall vs. decision boundary, were used to estimate the effectiveness of the proposed HGSOXGB model. The model achieves the highest accuracy of 97.39% and an area under the ROC curve of 99.10% compared with other classifiers. Additionally, the important features that significantly affect the prediction of ICU transfer need using XGB were calculated. |
| Keywords | COVID-19; hunger games search optimization; Metropolis–Hastings; ICU prediction; eXtreme gradient boosting |
| ANZSRC Field of Research 2020 | 460208. Natural language processing |
| Byline Affiliations | Khulna University, Bangladesh |
| University of Queensland | |
| International University of Business Agriculture and Technology, Bangladesh | |
| School of Mathematics, Physics and Computing | |
| Princess Nourah bint Abdulrahman University, Egypt | |
| Yeungnam University, Republic of Korea |
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