COVID-19 Patient Recovery Prediction Using Efficient Logistic Regression Model
International Conference on Cyber Security, Privacy and Networking, ICSPN 2022
; 599 LNNS:134-149, 2023.
Artigo
em Inglês
| Scopus | ID: covidwho-2284531
ABSTRACT
This research develops a COVID-19 patient recovery prediction model using machine learning. A publicly available data of infected patients is taken and pre-processed to prepare 450 patients' data for building a prediction model with 20.27% recovered cases and 79.73% not recovered/dead cases. An efficient logistic regression (ELR) model is built using the stacking of random forest (RF) and logistic regression (LR) classifiers. Further, the proposed model is compared with state-of-art models such as logistic regression (LR), support vector machine (SVM), decision tree (C5.0), and random forest (RF). All the models are evaluated with different metrics and statistical tests. The results show that the proposed ELR model is good in predicting not recovered/dead cases and handling imbalanced data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Texto completo:
Disponível
Coleções:
Bases de dados de organismos internacionais
Base de dados:
Scopus
Tipo de estudo:
Estudo prognóstico
Idioma:
Inglês
Revista:
International Conference on Cyber Security, Privacy and Networking, ICSPN 2022
Ano de publicação:
2023
Tipo de documento:
Artigo
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