Methods Inf Med 1997; 36(02): 160-162
DOI: 10.1055/s-0038-1634698
Original Article
Schattauer GmbH

Extraction of Rules for Tuberculosis Diagnosis Using an Artificial Neural Network

H. L. Viktor
1   Department of Informatics, University of Pretoria, South Africa
,
I. Cloete
2   Department of Computer Science, University of Stellenbosch, South Africa
,
N. Beyers
3   Department of Paediatrics and Child Health, University of Stellenbosch, South Africa
› Author Affiliations
Further Information

Publication History

Publication Date:
20 February 2018 (online)

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Abstract:

The treatment of tuberculosis (TB) is a major challenge throughout the world. The Western Cape Region of South Africa has the highest occurrence of TB in the world. Here, TB is increasing due to improperly managed treatment programmes and inadequate facilities. The development of rules to aid medical practitioners in the early and accurate diagnosis of tuberculosis should prove worthwhile. A method to extract such diagnostic rules from an artificial neural network is presented. These rules accurately represent the knowledge embedded in the “raw” TB data.