Kumar Rajagopal, Al-Turjman Fadi, Srinivas L N B, Braveen M, Ramakrishnan Jothilakshmi
Department of Electronics and Instrumentation Engineering, National Institute of Technology, Chumkedima, Dimapur, Nagaland 797103 India.
Artificial Intelligence Engineering Department, Research Centre for AI and IoT, Near East University, Nicosia, Mersin 10, Turkey.
Neural Comput Appl. 2023;35(10):7207-7220. doi: 10.1007/s00521-021-06412-w. Epub 2021 Sep 21.
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 with an accuracy of 86%. The study provides important information for public health providers and the government to control the COVID-19 epidemic.
2019冠状病毒病（COVID-19）是一场持续在全球范围内广泛爆发的公共卫生事件，影响着数百万人的健康，有时甚至会导致死亡。疫情预测并采取谨慎措施是防止COVID-19传播的唯一途径。本文提出了一种基于自适应神经模糊推理系统（ANFIS）的机器学习技术，用于预测印度可能出现的疫情爆发。所提出的基于ANFIS的预测系统根据从云计算获取的先前数据集来跟踪疫情的发展。所提出的ANFIS技术通过云数据集预测疫情高峰和COVID-19感染病例。本研究选择ANFIS是因为它兼具数值知识和语言知识，并且具有对数据进行分类和识别模式的能力。所提出的技术不仅能预测疫情爆发，还能跟踪疾病情况，并提出一项可衡量的政策来管理COVID-19疫情。所获得的预测结果表明，所提出的技术能非常有效地跟踪COVID-19疫情的发展。结果显示，感染率在2020年底下降，疫情高峰延迟了40至60天。使用所提出的ANFIS技术的预测结果显示均方误差（MSE）较低，为1.184×10，准确率为86%。该研究为公共卫生机构和政府控制COVID-19疫情提供了重要信息。
