A Belief Rule Based Expert System to Assess Tuberculosis under Uncertainty.
The primary diagnosis of Tuberculosis (TB) is usually carried out by looking at the various signs and symptoms of a patient. However, these signs and symptoms cannot be measured with 100 % certainty since they are associated with various types of uncertainties such as vagueness, imprecision, randomn...
| Published in: | Journal of Medical Systems Vol. 41; no. 3; pp. 1 - 12 |
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| Main Authors: | , , , |
| Format: | equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Springer Nature
Mar2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121441732&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121441732 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Mar2017 vid: 41 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 121441732 121441732 121441732 10.1007/s10916-017-0685-8 121441732 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: A Belief Rule Based Expert System to Assess Tuberculosis under Uncertainty. aug: au: Hossain, Mohammad Ahmed, Faisal Fatema-Tuj-Johora Andersson, Karl affil: Department of Computer Science and Engineering , University of Chittagong , Chittagong Bangladesh sug: subj: Expert Systems Tuberculosis Diagnosis Uncertainty Human Knowledge Bases User-Computer Interface Bangladesh ROC Curve Confidence Intervals Funding Source ab: The primary diagnosis of Tuberculosis (TB) is usually carried out by looking at the various signs and symptoms of a patient. However, these signs and symptoms cannot be measured with 100 % certainty since they are associated with various types of uncertainties such as vagueness, imprecision, randomness, ignorance and incompleteness. Consequently, traditional primary diagnosis, based on these signs and symptoms, which is carried out by the physicians, cannot deliver reliable results. Therefore, this article presents the design, development and applications of a Belief Rule Based Expert System (BRBES) with the ability to handle various types of uncertainties to diagnose TB. The knowledge base of this system is constructed by taking experts' suggestions and by analyzing historical data of TB patients. The experiments, carried out, by taking the data of 100 patients demonstrate that the BRBES's generated results are more reliable than that of human expert as well as fuzzy rule based expert system. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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