Comprehensive Performance Analysis on Classical Machine Learning and Deep Learning Methods for Predicting the COVID-19 Infections
Título del documento: Comprehensive Performance Analysis on Classical Machine Learning and Deep Learning Methods for Predicting the COVID-19 Infections
Revista: Computación y sistemas
Base de datos:
Número de sistema: 000560739
ISSN: 1405-5546
Autores: 1
1
Instituciones: 1Banaras Hindu University, Institute of Science, Uttar Pradesh. India
Año:
Periodo: Jul-Sep
Volumen: 26
Número: 3
Paginación: 1119-1135
País: México
Idioma: Inglés
Tipo de documento: Artículo
Resumen en inglés The COVID-19 (coronavirus disease) has been declared a pandemic throughout the world by the WHO (World Health Organization). The number of active COVID-19 cases is increasing day by day and clinical laboratory findings consume more time while interpreting the COVID-19 infected result. There are limited treatment facilities and proper guidelines for reducing infection rates. To overcome these limitations, the requirement of clinical decision support systems embedded with prediction algorithms is raised. In our study, we have architected the clinical prediction system using classical machine learning, deep learning algorithms, and experimental laboratory data. Our model estimated which patients were likely infected with COVID-19 disease. The prediction performances of our models are evaluated based on the accuracy score. The experimental dataset has been provided by Hospital Israelita Albert Einstein at Sao Paulo, Brazil, which included the records of 600 patients from 18 laboratory findings with 10% COVID-19 disease infected patients. Our model has been validated with a train-test split approach, 10-fold cross-validation, and AUC-ROC curve score. The experimental results show that the infected patients with COVID-19 disease are identified at an accuracy of 91.88% through the deep learning method (Convolutional Neural Network (CNN)) and 89.79 % through classical machine learning (Logistic Regression) respectively. This high accuracy is evidence that our prediction model could be readily used for predicting the COVID-19 infections and assisting the health experts in better diagnosis and clinical studies.
Disciplinas: Ciencias de la computación
Palabras clave: Inteligencia artificial
Keyword: Artificial intelligence
Texto completo: Texto completo (Ver HTML) Texto completo (Ver PDF)