Cargando…

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States

Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting...

Descripción completa

Detalles Bibliográficos
Autores principales: Guleria, Pratiyush, Ahmed, Shakeel, Alhumam, Abdulaziz, Srinivasu, Parvathaneni Naga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8775063/
https://www.ncbi.nlm.nih.gov/pubmed/35052249
http://dx.doi.org/10.3390/healthcare10010085
_version_ 1784636491749654528
author Guleria, Pratiyush
Ahmed, Shakeel
Alhumam, Abdulaziz
Srinivasu, Parvathaneni Naga
author_facet Guleria, Pratiyush
Ahmed, Shakeel
Alhumam, Abdulaziz
Srinivasu, Parvathaneni Naga
author_sort Guleria, Pratiyush
collection PubMed
description Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated.
format Online
Article
Text
id pubmed-8775063
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-87750632022-01-21 Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States Guleria, Pratiyush Ahmed, Shakeel Alhumam, Abdulaziz Srinivasu, Parvathaneni Naga Healthcare (Basel) Article Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India. The proposed classification model is applied to the recent COVID-19 dataset for India, and a performance evaluation of various state-of-the-art classifiers to the proposed model is performed. The classifiers forecasted the patients’ infection status in different regions to better plan resources and response care systems. The appropriate classification of the output class based on the extracted input features is essential to achieve accurate results of classifiers. The experimental outcome exhibits that the proposed Hybrid Model reached a maximum F1-score of 94% compared to Ensembles and other classifiers like Support Vector Machine, Decision Trees, and Gaussian Naïve Bayes on a dataset of 5004 instances through 10-fold cross-validation for predicting the right class. The feasibility of automated prediction for COVID-19 infection cure and death rates in the Indian states was demonstrated. MDPI 2022-01-02 /pmc/articles/PMC8775063/ /pubmed/35052249 http://dx.doi.org/10.3390/healthcare10010085 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guleria, Pratiyush
Ahmed, Shakeel
Alhumam, Abdulaziz
Srinivasu, Parvathaneni Naga
Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States
title Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States
title_full Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States
title_fullStr Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States
title_full_unstemmed Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States
title_short Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States
title_sort empirical study on classifiers for earlier prediction of covid-19 infection cure and death rate in the indian states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8775063/
https://www.ncbi.nlm.nih.gov/pubmed/35052249
http://dx.doi.org/10.3390/healthcare10010085
work_keys_str_mv AT guleriapratiyush empiricalstudyonclassifiersforearlierpredictionofcovid19infectioncureanddeathrateintheindianstates
AT ahmedshakeel empiricalstudyonclassifiersforearlierpredictionofcovid19infectioncureanddeathrateintheindianstates
AT alhumamabdulaziz empiricalstudyonclassifiersforearlierpredictionofcovid19infectioncureanddeathrateintheindianstates
AT srinivasuparvathaneninaga empiricalstudyonclassifiersforearlierpredictionofcovid19infectioncureanddeathrateintheindianstates