Search

Showing total 3,358 results
Start Over Topic machine learning Publication Year Range Last 10 years Publisher springer berlin heidelberg
3,358 results

175. Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system

Author

Zarnab Khalid, Uttam Ghosh, Celestine Iwendi, Kainaat Mahboob, Muhammad Rizwan, and Abdul Rehman Javed

Subjects
Coronavirus disease 2019 (COVID-19), Computer Networks and Communications, Computer science, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), SVM, Uncertain systems, 02 engineering and technology, medicine.disease_cause, Classifier (linguistics), Special Issue Paper, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Media Technology, medicine, ANFIS, Coronavirus, Adaptive neuro fuzzy inference system, business.industry, COVID-19, 020207 software engineering, Pattern recognition, Risk prediction, Support vector machine, Detection, Hardware and Architecture, Fatal disease, 020201 artificial intelligence & image processing, Artificial intelligence, business, Software, Information Systems
Abstract

Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity's body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease's risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.

Published
2021