Prognosis of COVID- 19 Patients with Machine Learning Techniques

Abstract

The pandemic has continued persistently over the period of one and half year due to spread of Novel Coronavirus. Every country in the world has been experiencing different periods of surges. These surges are called as coronavirus waves.  More lives have been affected during the second wave than the first.  Due to the overwhelming health care burden on hospitals during the second wave, people with mild symptoms are advised home quarantine by doctors.  People in home care need to be monitored continuously to know whether, they need further hospitalization, or they need any other medications, what are the readings of  their health factors like fever, oxygen levels etc. Also people who are hospitalized need to be monitored.  Machine learning techniques can provide better information to the health workers for patient care.  This research proposes  machine learning models which can identify the patient's condition into four classes: “Need home care”, “Completely cured”,” Need Hospitalization”, and ” Mortality”.  The models are trained with clinical data of COVID-19 patients. To train the model, four machine learning algorithms are used: K-nearest neighbor (KNN), Random Forest Tree (RFC), ExtratreeClassifier (ETC) and ensemble technique.  Further the models are validated using k-Fold validation during the training phase.  Experiments were carried out on ten clinical parameters which are sufficient to identify the status of COVID-19 patient. Results show that models have performed well with an accuracy of 98.77% (KNN), 98.51 % (ETC), 98.05 % (RFC), and 98.77%  (ENSEMBLE).  Prognosis of patients can assist the medical practitioners in making decisions related to health risks and identify the home quarantined patients who may need further hospitalization.