基于模糊的体系测定膝关节与骨关节炎的严重级(2)

Fuzzy-Based System for Determining the Severity Level of Knee Osteoarthritis

that will determine the level of severity of knee osteoarthritis so as to reduce the pain and physical disability associated with significant social and economical burden. This paper reports the development of a fuzzy system that allow to determine the level of severity of knee osteoarthritis, given some input conditions, the system was implemented and simulated using MATLAB 7.6.0 Fuzzy Logic Toolbox .

The remaining part of this paper is arranged as follows: a brief review of related works, presentation of a fuzzy logic controller, the fuzzy logic controller design, model simulation, discussion of results and conclusion.

II. Related Works

The first major commercial application of fuzzy logic was in the area of cement kiln control, an operation which requires that an operator monitor four internal states of the kiln, control four sets of operations, and dynamically manage 40 or 50 "rules of thumb" about their interrelationships, all with the goal of controlling a highly complex set of chemical interactions. One such rule is "If the oxygen percentage is rather high and the free-lime and kiln-drive torque rate is normal, decrease the flow of gas and slightly reduce the fuel rate" [1]. In [8], a fuzzy logic temperature controller for preterm Neonate incubator was designed. The goal was to attain thermoneutrality and also the efficient

stabilization of the incubator temperature at a desired value and thereby prevention of hypothermia/hyperthermia related diseases/conditions and death. Fuzzy Logic modeling has been used in many medical applications [9]. The medical decision-making process is often complicated which makes it an attractive area for fuzzy systems modeling. It has been proposed that health and disease as concepts are best understood as fuzzy sets and that illness and wellness are really fuzzy states of health. [10] describes a fuzzy-logic-based expert system for the classification of sleep disorders where fuzzy sets are used to qualify‘ diagnostic criteria in terms of frequency, intensity, quantity and graduated yes–no variables. Warren et al [11] presented a decision support system for automating the application of clinical practice guidelines based on fuzzy methods. Other fuzzy diagnostic systems have been reported, for example, in rheumatoid arthritis [9]. This research work offers an alternative and often complementary approach to conventional approaches to modeling system.

III. Fuzzy Logic Controller

Generally, a Fuzzy logic Controller comprises of four principal

components: a fuzzification interface, a knowledge base, decision-making logic, and a defuzzification interface [12]. The figure below shows the basic configuration of a fuzzy logic controller.

Fig. 1: Major Components of a fuzzy-based system Adapted from [13]

A. The Fuzzification interface

The fuzzification converts the input data namely KneePain, Stiffness, Swelling and Crepitus into suitable linguistic variables. A scale mapping is performed using

triangular membership function, which transfers the range of input variables into corresponding universe of discourse.