- Tytuł:
- Applying Hunger Game Search (HGS) for selecting significant blood indicators for early prediction of ICU COVID-19 severity
- Autorzy:
-
Sayed, Safynaz AbdEl-Fattah
ElKorany, Abeer
Sayed, Sabah - Powiązania:
- https://bibliotekanauki.pl/articles/27312915.pdf
- Data publikacji:
- 2023
- Wydawca:
- Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
- Tematy:
-
ICU severity prediction
COVID-19
clinical blood tests
Hunger Game search
HGS
optimization algorithm
support vector machine
SVM
feature selection - Opis:
- This paper introduces an early prognostic model for attempting to predict the severity of patients for ICU admission and detect the most significant features that affect the prediction process using clinical blood data. The proposed model predicts ICU admission for high-severity patients during the first two hours of hospital admission, which would help assist clinicians in decision-making and enable the efficient use of hospital resources. The Hunger Game search (HGS) meta-heuristic algorithm and a support vector machine (SVM) have been integrated to build the proposed prediction model. Furthermore, these have been used for selecting the most informative features from blood test data. Experiments have shown that using HGS for selecting features with the SVM classifier achieved excellent results as compared with four other meta-heuristic algorithms. The model that used the features that were selected by the HGS algorithm accomplished the topmost results (98.6 and 96.5%) for the best and mean accuracy, respectively, as compared to using all of the features that were selected by other popular optimization algorithms.
- Źródło:
-
Computer Science; 2023, 24 (1); 113--136
1508-2806
2300-7036 - Pojawia się w:
- Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł