An Expert System for Clinical Risk Assessment of Polycystic Ovary Syndrome under Uncertainty

Abstract

Abstract This paper describes a prototype clinical expert system for risk stratification of patients with polycystic ovary syndrome (PCOS). Polycystic ovary syndrome (PCOS) is the most common hormonal disorder among women of reproductive age. It is a heterogeneous disorder of uncertain cause. Since the symptoms of PCOS are seemingly unrelated to one another the condition is often overlooked and undiagnosed. The determination of accurate degree or intensity of PCOS signs is difficult for the physician. Hence, the accuracy of the diagnostic process is difficult to achieve. The signs and symptoms of PCOS are usually expressed in qualitative and quantitative ways. Since the qualitative factors cannot measure in a quantitative way, various types of uncertainties may occur, such as incompleteness, vagueness and imprecision. For that, it is necessary to address the issue of uncertainty by using appropriate methodology. However, no existing system is able to address this issue of uncertainty. Therefore, this paper demonstrates the application of a novel method, named belief rule-based inference methodology-RIMER; this prototype can deal with 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 PCOS by taking account of signs and symptoms.