An Expert System to Detect Uterine Cancer under Uncertainty
Muhammed J. A. Patwary, Subrina Akter, Tanjim Mahmud · IOSR Journal of Computer Engineering · 2014
Uterine cancer is one of the conspicuous cancers for women in both developed and developing countries including Bangladesh.Now, in the world it is the sixth most common cancer among women and fourteenth most common cancer overall.The high occurrence of uterine cancer in women has increased significantly in the last years.This involves many factors to be measured and evaluated, which are related to the signs and symptoms of this disease.These factors usually expressed in quantitative and qualitative ways.In addition, a hierarchical relationship exists among these factors.Since qualitative factors cannot be measured in a quantitative way, resulting various types of uncertainties such as incompleteness, vagueness, imprecision.Therefore, it is necessary to address the issue of uncertainty by using appropriate methodology; otherwise, the conclusion to detect uterine cancer will become inaccurate.There exist many systems to address the issue presented in this paper.However, none of them is able to address the issue of uncertainty.Therefore, this paper demonstrates the application of a novel method, named belief rule-based inference methodology-RIMER, which is capable of addressing the uncertainties in both clinical domain knowledge and clinical data.This paper reports the development of a Belief Rule Based Expert System (BRBES) using RIMER approach, which is capable of detecting the presence of uterine cancer by taking account of signs and symptoms.The system has been validated by using real patient data and it has been observed that the results generated by the BRBES are more reliable than the manual system usually carried out by a physician.