Addressing Design Issues in Medical Expert System for Low Back Pain
  Management: Knowledge Representation, Inference Mechanism, and Conflict
  Resolution Using Bayesian Network
  Aiming at developing a medical expert system for low back pain management,
the paper proposes an efficient knowledge representation scheme using frame
data structures, and also derives a reliable resolution logic through Bayesian
Network. When a patient comes to the intended expert system for diagnosis, the
proposed inference engine outputs a number of probable diseases in sorted
order, with each disease being associated with a numeric measure to indicate
its possibility of occurrence. When two or more diseases in the list have the
same or closer possibility of occurrence, Bayesian Network is used for conflict
resolution. The proposed scheme has been validated with cases of empirically
selected thirty patients. Considering the expected value 0.75 as level of
acceptance, the proposed system offers the diagnostic inference with the
standard deviation of 0.029. The computational value of Chi-Squared test has
been obtained as 11.08 with 12 degree of freedom, implying that the derived
results from the designed system conform the homogeneity with the expected
outcomes. Prior to any clinical investigations on the selected low back pain
patients, the accuracy level (average) of 73.89% has been achieved by the
proposed system, which is quite close to the expected clinical accuracy level
of 75%.
