Early detection of ARDS in the Covid patients

Abstract

COVID-19 acute respiratory distress syndrome or ARDS affects both male and female and causes severity which can led to death. This is different from pneumonia but causes a serious breathing problem in the patients. The Covid-19 patients who fulfil Berlin criteria, are diagnosed with ARDS. A higher death rate is linked to a lack of awareness of severe respiratory issues symptoms and a failure to seek competent medical care early or late in the disease's progression. Therefore, it is highly necessary to take early precautions at the initial stage such that it’s symptoms and effect can be found at early stage for better diagnosis. Machine learning now days has a great influence in the health care sector because of its high computational capability for early prediction of the diseases with accurate data analysis. In our paper we have analyzed various machine learning classifiers techniques to classify data of severe and moderate COVID-19 ARDS patients. The input data is prepossessed and converted in to binary form. The comparison technique reveals that the proposed Logistic Regression and Decision Tree classifier have resulted with a great accuracy of 88% and considered as the effective classifier techniques for severe and moderate prediction.