基于模糊的体系测定膝关节与骨关节炎的严重级(3)

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

B. Knowledge Base

The knowledge base consists of database and rule base. The database provides necessary definitions that are used to define linguistic control rules with syntax, such as: IF <fuzzy proportion> THEN <fuzzy proportion>. The IF‘ part is called the antecedent‘ and the THEN‘ part is called the consequent‘. In this work, the antecedents are ―kneePain‘, stiffness‘, crepitus‘ and age‘ and the consequent is the severitylevel‘.

C. Decision Making Logic

The decision making logic infers a system of rules through the fuzzy operator AND‘ and generates a single truth value which determines the outcome of the rules (inferred fuzzy control action).

D. Defuzzification

Defuzzification is a process in which membership functions are sampled to find the grade of membership; this grade is then used in the fuzzy logic equation(s) and an outcome region is defined, from which the output is deduced. Over the years, several defuzzification techniques have been suggested. The most frequently used ones are the centroid or centre of area, centre of sums, and mean of maxima.

IV. The Fuzzy Logic Controller Design

During fuzzification, the fuzzy input variable, KneePain ranging from 1 to 10 is converted into four linguistic grades namely Grade1, Grade2, Grade3 and Grade4. Similarly, the input variable Age ranging from 45 to 75 is converted into five linguistic variables namely: VeryYoung, Young, MiddleAge, Old and VeryOld. The other two fuzzy input variables; Stiffness and Crepitus and the output variable SeverityLevel ranging from 1 to 10 are converted into five linguistic levels namely: VeryMild, Mild, Moderate, Severe and VerySevere. The triangular membership function is used to perform the scale mapping.

A. The Controller Inputs

The function of the controller is to determine the severity level of knee osteoarthritis which can be determined by knee pain (the major determinant) and any other three of the knee osteoarthritis symptoms. The researcher made use of knee pain, which is graded into four grades:

Grade1 when the patient can ignore the pain without taking drugs or pain reliever

Grade2 when the patient can ignore the pain by taking drugs once in a while

Grade3 when the patient can only ignore the pain by consistently taking of drugs

Grade4 when the patient cannot ignore pain, even while consistently taking drugs.

The inputs Stiffness, and Crepitus use the linguistic variables VeryMild, Mild, Moderate, Severe and VerySevere MF using the triangular MF formulation, while the last input variable Age uses the linguistic variables VeryYoung, Young, MiddleAge, Old and VeryOld.

B. The Controller Output

In this research work, MISO fuzzy system is applied, hence the only output variable used in this work is SeverityLevel which determine the level of severity of knee osteoarthritis giving the input variables.

C. The controller’s Linguistic Rules (Fuzzy IF-THEN Rules)

Based on the choice of inputs and output as discussed in section 3.3.1 and 3.3.2, the following fuzzy IF-THEN rules are extracted from the set of rules that represents an expert knowledge about how best to determine the severity level of knee osteoarthritis

IF (kneepain is grade4) and (stiffness is verysevere) and (crepitus is severe) and (age is veryYoung) THEN (severitylevel is verysevere).

IF (kneepain is grade3) and (stiffness is severe) and (crepitus is severe) and (age is young) THEN (severitylevel is severe).

IF (kneepain is grade2) and (stiffness is mild) and (crepitus is verymild) and (age is middleage) THEN (severitylevel is mild).

IF (kneepain is grade3) and (stiffness is verysevere) and (crepitus is moderate) and (age is veryYoung) THEN (severitylevel is severe).

IF (kneepain is grade4) and (stiffness is moderate) and (crepitus is moderate) and (age is veryYoung) THEN (severitylevel is severe).

D. Decision Making Logic

The decision making logic infers a system of rules through the fuzzy operator AND‘ and generates a single truth value which determines the outcome of the rules (inferred fuzzy control action).

E. Defuzzification

Defuzzification is a process in which membership functions are sampled to find the grade of membership; this grade is then used in the fuzzy logic equation(s) and an outcome region is defined, from which the output is deduced. Over the years, several defuzzification techniques have been suggested. The most frequently used ones are the centroid or centre of area, centre of sums, and mean of maxima.