基于模糊的体系测定膝关节与骨关节炎的严重级(6)

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

VII. Discussion of Result

When the parameter ―kneepain‖ was plotted against the variable ―stiffness‖, it was clearly observed that the higher the kneepain and stiffness, the higher the severity level (severitylevel) of the system and vice versa. The model takes into cognizance factors like crepitus and age. Peak requirement for high severity level is when both the kneepain and the stiffness are verysevere‘ . The importance of fuzzy inference is the ability to combine the effect of multiple factors and come up with a holistic view of the prevalent scenario. In the figure 4, the system combines adequately factors like kneepain‘, crepitus‘ and age‘ amongst other and present the fuzzified results in the level of severity. The presented simulated results are in three-dimensions. This is because presently it is difficult to represent higher dimensions without distorting the figure in Matlab tools. This has limited the number of variables to be considered to two against the fixed variable (output). It has not in any way however hindered the functionality of the system because each factor represented is depicted as an integral part of the whole system whose variables has been fuzzified.

Similarly, when ―age‖ was plotted against ―kneepain‖, the peak of this appeared only when kneepain is in grade4 and age is above 60 years as represented in figure 5 and this accounts for why knee osteoarthritis is common among older adults.

Other combinations of the input variables can be generated in similar manner. The evaluation of the severity level shows a system that can effectively handle the dynamic symptoms for determining severity level in patient with knee osteoarthritis and it also assist the physician in taking appropriate measure and educate the patient on how to go about managing the different severity level to avoid further injury to the knee. The computer based systems were used to analyze using different assumed values and the output shows that the software is robust enough for the determination of severity level in knee osteoarthritis. VIII.

Conclusion

This paper has presented a fuzzy inference system designed to determine the severity level based on identified factors (input variables). Knee osteoarthritis has no cure, but if diagnosed on time, it can be managed. With fuzzy based system, severity level can be determined and the ailment properly managed based on the usage of linguistic variables and the membership function developed for them. As opposed to expert systems, fuzzy system employs linguistic variables which facilitate human description using their natural language. With this development, knee osteoarthritis symptoms only need to be inputted in natural language term and not precise values. The results are entrusting and promising based on the flexibility and case of adaptability.

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