EARLY COVID PREDICTION: NEURO FUZZY MULTI-LAYERED DATA CLASSIFIER

EARLY COVID PREDICTION: NEURO FUZZY MULTI-LAYERED DATA CLASSIFIER Abstract Coronavirus disease (COVID-19) is a harmful disease caused from new SARS-CoV-2 virus. COVID-19 disease includes the symptoms such as cold, cough, fever and difficulty in breathing. COVID-19 has affected many countries and their spread in world has put humanity at risk. Due to increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the corona virus disease existence in patients. However, the accuracy was not improved and time consumption was not minimized during the disease prediction. In order to address these problems, Least Square Regressive Gaussian Neuro Fuzzy Multi-Layered Data Classification (LSRGNFM-LDC) Technique is introduced in this article. LSRGNFM-LDC Technique performs efficient COVID prediction with better accuracy and lesser time consumption through feature selection and classification. Deming Least Square Regressive Feature Selection process is carried out for selecting the most relevant features through identifying the line of best fit. After the feature selection process, the data classification is carried out using neuro-fuzzy classifier with help of fuzzy if-then rules for performing prediction process. Finally, the patient data is predicted as the higher risk patient data, medium risk patient data or higher risk patient data in more accurate manner with higher accuracy and lesser time consumption. Experimental evaluation is performed by Novel Corona Virus 2019 Dataset using different metrics like prediction accuracy, prediction time and error rate. The result shows that LSRGNFM-LDC Technique improves the accuracy and minimizes the time consumption as well as error rate than existing works during COVID prediction. 1| P a g e EARLY COVID PREDICTION: NEURO FUZZY MULTI-LAYERED DATA CLASSIFIER Diagram Diagram EARLY COVID PREDICTION: NEURO FUZZYMULTI-LAYERED DATA d L Pedorm ' Const regressiori - the f process rules Fuzzy Pedni he efficient d6da prediction Fig 1: EARLY COVID PREDICTION II P a g e