Symptoms based Early Clinical Diagnosis of COVID-19 Cases using Hybrid and Ensemble Machine Learning Techniques

Abstract

This paper aims to develop a classification system to distinguish COVID-19 positive and negative cases based on common symptoms and could be used as a first-level screening tool for early detection of mild cases. Accordingly, existing classification models such as Logistic Regression, Gradient Boosting (GB), Random Forest (RF) and K-Nearest Neighbours have been tried on the COVID-19 symptoms dataset to identify the best performing model. Although traditional machine learning models provide promising results in terms of accuracy, precision and recall, this paper analyses the possibilities of improvement in classification results through ensemble and hybrid approaches. It is observed from the results that K-mode clustering followed by classification-based hybrid modelling resulted in improved classification accuracy in the clusters leading to an average accuracy of 87.17% and 87.24% with GB and RF respectively. Finally, the MaxVoting ensemble model, comprising GB and RF algorithms further boosted the accuracy to almost 90%.