Author/Authors :
ukaoha, kingsley c. university of benin - department of computer science, Benin City, Nigeria , ademiluyi, oluwadamilola university of benin - department of computer science, Benin City, Nigeria , ndunagu, juliana national open university of nigeria - department of computer science, Abuja, Nigeria , daodu, stephen s. university of benin - department of computer science, Benin City, Nigeria , osang, frank national open university of nigeria - department of computer science, Abuja, Nigeria
Abstract :
Coronaviruses which are positively sensed single-stranded Ribonucleic Acid (RNA) viruses are causing serious threat to global public health due to the widespread of the virus and no one having immunity to the virus. Timely diagnosis of the disease has become a major challenge due to the limitation associated with the present methods used in diagnosing of COVID-19 and a limited number of COVID-19 test kits available in hospitals due to the increasing number of cases daily. There is a need to propose a model that can provide timely, differential and alternative diagnosis option to prevent COVID-19 spreading among people. In this study an ANFIS based model was proposed for diagnosing COVID-19, the model was trained and tested using 600 COVID-19 dataset. The ANFIS model had accuracy of 96.6% for predicting and diagnosing COVID-19.
Keywords :
Coronavirus , COVID , 19 , Diagnose , ANFIS
