ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India

Rajagopal Kumar; Fadi Al-Turjman; L N B Srinivas; M Braveen; Jothilakshmi Ramakrishnan

doi:10.1007/s00521-021-06412-w

ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India

Neural Comput Appl. 2023;35(10):7207-7220. doi: 10.1007/s00521-021-06412-w. Epub 2021 Sep 21.

Authors

Rajagopal Kumar¹, Fadi Al-Turjman², L N B Srinivas³, M Braveen⁴, Jothilakshmi Ramakrishnan⁵

Affiliations

¹ Department of Electronics and Instrumentation Engineering, National Institute of Technology, Chumkedima, Dimapur, Nagaland 797103 India.
² Artificial Intelligence Engineering Department, Research Centre for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey.
³ Department of Information Technology, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203 India.
⁴ Department Computer Science Engineering, VIT, Chennai, 600127 India.
⁵ Department of Mathematics, Mazharul Uloom College, Ambur, India.

Abstract

Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India. The proposed ANFIS-based prediction system tracks the growth of epidemic based on the previous data sets fetched from cloud computing. The proposed ANFIS technique predicts the epidemic peak and COVID-19 infected cases through the cloud data sets. The ANFIS is chosen for this study as it has both numerical and linguistic knowledge, and also has ability to classify data and identify patterns. The proposed technique not only predicts the outbreak but also tracks the disease and suggests a measurable policy to manage the COVID-19 epidemic. The obtained prediction shows that the proposed technique very effectively tracks the growth of the COVID-19 epidemic. The result shows the growth of infection rate decreases at end of 2020 and also has delay epidemic peak by 40-60 days. The prediction result using the proposed ANFIS technique shows a low Mean Square Error (MSE) of 1.184 × 10^-3 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

Keywords: Cloud data; Corona-virus disease 19 (COVID-19); Edge artificial intelligence; Machine learning.