A Genetic-Neuro-Fuzzy inferential model for diagnosis of tuberculosis

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• 作者：Mumini Olatunji Omisore^a ; ^{ootsorewilly@gmail.com} ; Oluwarotimi Williams Samuel^b ; ¹ ; ^{timitex92@gmail.com} ; Edafe John Atajeromavwo^c ; ² ; ^{edafejohn2006@yahoo.com}
• 关键词：Medical diagnosis ; Mycobacterium tuberculosis ; Artificial intelligence ; Inference system ; Decision support
• 刊名：Applied Computing and Informatics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：13
• 期：1
• 页码：27-37
• 全文大小：1017 K
• 卷排序：13

文摘

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.