A New COVID-19 Patient Detection Strategy Based on Hidden Naïve Bayes Classifier

Abstract

COVID-19 is a universal infectious disease recognized first by people with influenza and bacterial pneumonia symptoms in Wuhan, Hubei Province. Currently, a new mutated disease has the same symptoms as COVID-19 and influenza and causes dangerous infections in the body. Due to the fact that these two diseases share some diagnostic features and symptoms in common with one another, healthcare workforces require aid and support in predicting patients' conditions. This was done by using machine learning methods in diagnosis. From this point, this paper proposes a diagnostic model to detect patients' symptoms and classify them into one of five disease groups, utilizing Neighborhood Component Analysis (NCA) as a feature selection method and the Hidden Naïve Bayes (HNB) method as a multiclass classifier. This paper suggests the model consists of two significant phases: the pre-processing phase (cleaning, normalization, and discretization) and the classification phase. Conducting the COVID-19 dataset, the experimental findings showed that the suggested multi-class model had 89% accuracy for disease diagnoses. Furthermore, according to the patient’s symptoms, the proposed classification model led to a good diagnosis for the mutated COVID-19 disease.