Predicting Hospitals Hygiene Rate during COVID-19 Pandemic

Abdulrahman M Qahtani,Samah Abuayeid,Abdullah Baz,Bader M,Hosam Alhakami

doi:10.14569/ijacsa.2020.0111294

Abdulrahman M Qahtani, Samah Abuayeid + Show 3 more

Open Access

https://doi.org/10.14569/ijacsa.2020.0111294

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Abstract

COVID-19 pandemic has reached global attention with the increasing cases in the whole world. Increasing awareness for the hygiene procedures between the hospital’s staff, and the society became the main concern of the World Health Organization (WHO). However, the situation of COVID-19 Pan-demic has encouraged many researchers in different fields to investigate to support the efforts offered by the hospitals and their health practitioners. The main aim of this research is to predict the hospital’s hygiene rate during COVID-19 using COVID-19 Nursing Home Dataset. We have proposed a feature extraction, and comparing the results estimating from K-means clustering algorithm, and three classification algorithms: random forest, decision tree, and Naive Bayes, for predicting the hospital’s hygiene rate during COVID-19. However, the results show that classification algorithms have addressed better performance than K-means clustering, in which Naive Bayes considered the best algorithm for achieving the research goal with accuracy value equal to 98.1%. AS a result the research has discovered that the hospitals that offered weekly amounts of personal protective equipment (PPE) have passed the personal quality test, which lead to a decrease in the number of COVID-19 cases between the hospital’s staff.

Highlights

World Health Organization (WHO) has faced many challenges in increasing the global healthcare and Hygiene awareness to overcome COVID-19 pandemic
The results show that classification algorithms have addressed better performance than K-means clustering, in which Naive Bayes considered the best algorithm for achieving the research goal with accuracy value equal to 98.1%
AS a result the research has discovered that the hospitals that offered weekly amounts of personal protective equipment (PPE) have passed the personal quality test, which lead to a decrease in the number of COVID-19 cases between the hospital’s staff

Summary

Introduction

WHO has faced many challenges in increasing the global healthcare and Hygiene awareness to overcome COVID-19 pandemic. There is a high demeaned in increasing the rate hospitals hygiene during COVID-19 pandemic to protect hospitals staff and hospital patients from COVID-19 infection. PPE can provide safe health-care services for hospital patients during COVID-19 pandemic. WHO and all followed health ministries in whole world have increased there continues concern about offering all hygiene requirements, and tests for the hospitals to ensure of a high health safety level during the pandemic. Relatively, introducing machine learning algorithms and methods in predicting the rate of hospitals hygiene can has great impact on enhancing hospitals and health fields services especially during COVID-19. We have proposed a feature extraction, and comparing the results estimating from K-means clustering algorithm, and three classification algorithms: random forest, decision tree, and Naive Bayes, for predicting the hospital’s hygiene rate during COVID-19 using COVID-19 Nursing Home Dataset.

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