Temporal logic–based fuzzy decision support system for rheumatic fever and rheumatic heart diseases in Nepal

Abstract

Decision Support Systems (DSSs) have been used in many fields as a tool to support decision making at different levels in an organisation. However, use of DSSs in medical diagnosis is always hampered by levels of uncertainty in that observed symptoms cannot be precisely described. It is this inability to describe observed symptoms precisely that necessitates our approach to develop a DSS for diagnosing Rheumatic Fever (RF) and Rheumatic Heart Disease (RHD) using Fuzzy and Temporal logic. Developing a decision support system for RF/RHD is complex due to the level of vagueness, complexity and uncertainty management involved, especially when the same symptoms can indicate multiple diseases. In this paper we describe how fuzzy logic could be applied to the development of a DSS that could be used for diagnosing arthritis pain (diagnosis of arthritis pain for rheumatic fever patient only), in four different stages namely Fairly Mild, Mild, Moderate and Severe. Our diagnostic tool allows doctors to log in symptoms describing arthritis pain using numerical values that are estimates of the severity of the pain a patient feels. These values are used as input parameters to the fuzzy logic tool box, which invokes rules in the knowledge to determine a value of severity for the arthritis pain. This fuzzy logic uses rules in the knowledge-based to determine whether the symptoms logged describe arthritis as being fairly mild, mild, moderate or severe. Our approach employs a knowledge base that was built using WHO guidelines for diagnosing RF, Nepal country guidelines and a Matlab fuzzy tool box as components to the system