文章基本信息

标题：A Genetic-Neuro-Fuzzy inferential model for diagnosis of tuberculosis
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作者：Mumini Olatunji Omisore ; Oluwarotimi Williams Samuel ; Edafe John Atajeromavwo 等
期刊名称：Applied Computing and Informatics
印刷版ISSN：2210-8327
电子版ISSN：2210-8327
出版年度：2017
卷号：13
期号：1
页码：27-37
DOI：10.1016/j.aci.2015.06.001
出版社：Elsevier
摘要：Tuberculosis is a social, re-emerging infectious disease with medical implications throughout the globe. Despite efforts, the coverage of tuberculosis disease (with HIV prevalence) in Nigeria rose from 2.2% in 1991 to 22% in 2013 and the orthodox diagnosis methods available for Tuberculosis diagnosis were been faced with a number of challenges which can, if measure not taken, increase the spread rate; hence, there is a need for aid in diagnosis of the disease. This study proposes a technique for intelligent diagnosis of TB using Genetic-Neuro-Fuzzy Inferential method to provide a decision support platform that can assist medical practitioners in administering accurate, timely, and cost effective diagnosis of Tuberculosis. Performance evaluation observed, using a case study of 10 patients from St. Francis Catholic Hospital Okpara-In-Land (Delta State, Nigeria), shows sensitivity and accuracy results of 60% and 70% respectively which are within the acceptable range of predefined by domain experts.
关键词：Medical diagnosis ; Mycobacterium tuberculosis ; Artificial intelligence ; Inference system ; Decision support