A comparative study for determining Covid-19 risk levels by unsupervised machine learning methods.
Expert Syst Appl
; 190: 116243, 2022 Mar 15.
Article
en En
| MEDLINE
| ID: mdl-34815623
ABSTRACT
The restrictions have been preferred by governments to reduce the spread of Covid-19 and to protect people's health according to regional risk levels. The risk levels of locations are determined due to threshold values ââbased on the number of cases per 100,000 people without environmental variables. The purpose of our study is to apply unsupervised machine learning techniques to determine the cities with similar risk levels by using the number of cases and environmental parameters. Hierarchical, partitional, soft, and gray relational clustering algorithms were applied to different datasets created with weekly the number of cases, population densities, average ages, and air pollution levels. Comparisons of the clustering algorithms were performed by using internal validation indexes, and the most successful method was identified. In the study, it was revealed that the most successful method in clustering based on the number of cases is Gray Relational Clustering. The results show that using the environmental variables for restrictions requires more clusters than 4 for healthier decisions and Gray Relational Clustering gives stable results, unlike other algorithms.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Expert Syst Appl
Año:
2022
Tipo del documento:
Article
País de afiliación:
Turquía