Improving performance of classifiers for diagnosis of critical diseases to prevent COVID risk.

ABSTRACT

The risk of developing COVID-19 and its variants may be higher in those with pre-existing health conditions such as thyroid disease, Hepatitis C Virus (HCV), breast tissue disease, chronic dermatitis, and other severe infections. Early and precise identification of these disorders is critical. A huge number of patients in nations like India require early and rapid testing as a preventative measure. The problem of imbalance arises from the skewed nature of data in which the instances from majority class are classified correct, while the minority class is unfortunately misclassified by many classifiers. When it comes to human life, this kind of misclassification is unacceptable. To solve the misclassification issue and improve accuracy in such datasets, we applied a variety of data balancing techniques to several machine learning algorithms. The outcomes are encouraging, with a considerable increase in accuracy. As an outcome of these proper diagnoses, we can make plans and take the required actions to stop patients from acquiring serious health issues or viral infections.