Deming least square regressed feature selection and Gaussian neuro‐fuzzy multi‐layered data classifier for early COVID prediction

Mydukuri, Rathnamma V; Kallam, Suresh; Patan, Rizwan; Al‐Turjman, Fadi; Ramachandran, Manikandan

Coronavirus disease (COVID‐19) is a harmful disease caused by the new SARS‐CoV‐2 virus. COVID‐19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID‐19 has affected many countries and their spread in the world has put humanity at risk. Due to the increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the coronavirus disease existence in patients. However, the accuracy was not improved, and time consumption was not minimized during the disease prediction. 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. The preprocessing is used to eliminate the unwanted data in input features. Preprocessing is applied to reduce the time complexity. Next, 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, Gaussian neuro‐fuzzy classifier in LSRGNFM‐LDC technique performs the data classification process with help of fuzzy if‐then rules for performing prediction process. Finally, the fuzzy if‐then rule classifies the patient data as lower risk level, medium risk level and higher risk level 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.