Adaptive Neuro Fuzzy Inference System for Diagnosing Coronavirus Disease 2019 (COVID-19) | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Article 1, Volume 20, Issue 2, December 2020, Page 1-31 PDF (2.08 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2020.40518.1027 | ||||
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| Authors | ||||
Kingsley Chiwuike Ukaoha  1; Oluwadamilola Ademiluyi2; Juliana Ndunagu3; Stephen Segun Daodu2; Frank Osang3
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| 1Department of Computer Science University of Benin Benin City, Nigeria. | ||||
| 2Department of Computer Science University of Benin Benin City, Nigeria | ||||
| 3Department of Computer Science National Open University of Nigeria 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 infection rate of the virus and there is no immunity to the virus or known cure yet. Timely diagnosis of the disease has become a major challenge due to the limitation associated with the present methods used in diagnosis 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 120 diagnosed COVID-19 dataset. The ANFIS model had accuracy of 99.6% compared to all other models used for predicting and diagnosing COVID-19 and we are optimistic it would be quite useful to the health sector. | ||||
| Keywords | ||||
| Coronavirus detection; COVID-19 diagnosis; ANFIS model | ||||
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