Predicting Hospitals Hygiene Rate during COVID-19 Pandemic

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 Pandemic 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.