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A Belief Rule Base Expert System for staging Non-Small Cell Lung Cancer under Uncertainty
Department of Computer Science and Engineering, BGC Trust University Bangladesh, Bidyanagar, Chandanaish, Bangladesh.
BGC Trust University Bangladesh, Chandanaish, Chittagong-4381, Bangladesh.
University of Chittagong, Bangladesh. (Department of Computer Science and Engineering, BGC Trust University, Bangladesh)
University of Chittagong, Bangladesh.ORCID-id: 0000-0002-7473-8185
Vise andre og tillknytning
2019 (engelsk)Inngår i: Proceedings of 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), 2019Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Non small cell Lung cancer (NSCLC) is one of the most well-known types of Lung cancer which is reason for cancer related demise in Bangladesh. The early detection stage of NSCLC is required for improving the survival rate by taking proper decision for surgery and radiotherapy. The most common factors for staging NSCLC are age, tumor size, lymph node distance, Metastasis and Co morbidity. Moreover, physicians’ diagnosis is unable to give more reliable outcome due to some uncertainty such as ignorance, incompleteness, vagueness, randomness, imprecision. Belief Rule Base Expert System (BRBES) is fit to deal with above mentioned uncertainty by applying both Belief Rule base and Evidential Reasoning approach .Therefore, this paper represents the architecture, development and interface for staging NSCLC by incorporating belief rule base as well as evidential reasoning with the capability of handling uncertainty. At last, a comparative analysis is added which indicate that the outcomes of proposed expert system is more reliable and efficient than the outcomes generated from traditional human expert as well as Support Vector Machine (SVM) or Fuzzy Rule Base Expert System (FRBES).

sted, utgiver, år, opplag, sider
2019.
Emneord [en]
Non-Small Cell Lung Cancer (NSCLC), Expert System, Uncertainty, Belief rules base, Evidential Reasoning
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Identifikatorer
URN: urn:nbn:se:ltu:diva-76793OAI: oai:DiVA.org:ltu-76793DiVA, id: diva2:1371804
Konferanse
2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)
Tilgjengelig fra: 2019-11-20 Laget: 2019-11-20 Sist oppdatert: 2019-12-06

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Andersson, Karl

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