Case Based Reasoning Covid-19 Virus Detection Framework

Tessema, Mekrie Jemeber

dc.contributor.author	Tessema, Mekrie Jemeber
dc.date.accessioned	2022-03-09T06:41:55Z
dc.date.available	2022-03-09T06:41:55Z
dc.date.issued	2021-09
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/13177
dc.description.abstract	As time goes, application areas of information and communication technologies are growing dramatically as well. Technology advancements have a rapid effect on every field of life, be it medical field or any other field. COVID-19 (newly identified corona virus, SARS-CoV-2) is the disease caused by the new corona virus that emerged in China in December 2019. The epidemic could spread beyond the world unless countries follow the WHO's precautionary measures. COVID-19 symptoms include cough, fever or chills, shortness of breath or difficulty breathing, muscle or body aches, sore throat, new loss of taste or smell, diarrhea, headache, new fatigue, nausea or vomiting and congestion or runny nose. This study examines the model of diagnose of covid-19 disease in the domain of Ethiopian covid-19 testing centers. The required knowledge for this study was collected through interview and document analysis from domain experts, covid-19 patient history cards and other related relevant documents. Decision tree is used to model the acquired knowledge. The designed framework is evaluated using various techniques such as statistical analysis, comparative evaluation, and user acceptance evaluation. The retrieval performance of the framework is primarily measured by precision and recall, while the reuse performance is also evaluated using accuracy measurement, yielding an average result of 81% precision, 90% recall, and 90% accuracy. End users who are experts in the specific domain area evaluated the framework, and an average score of 79.16% was recorded.	en_US
dc.language.iso	en_US	en_US
dc.subject	computer science	en_US
dc.title	Case Based Reasoning Covid-19 Virus Detection Framework	en_US
dc.type	Thesis	en_US