Lattice-Based Fuzzy Medical Expert System for Low Back Pain Management

TL;DR

This paper presents a novel lattice-based fuzzy expert system for diagnosing and managing Low Back Pain, effectively handling imprecise clinical data through modular knowledge representation and fuzzy inference.

Contribution

It introduces a lattice-based fuzzy knowledge representation scheme and a modular design for a fuzzy expert system tailored to Low Back Pain management.

Findings

Prototype system built and tested with real patient data

System's inference results accepted by medical experts

Effective handling of imprecise clinical information

Abstract

Low Back Pain (LBP) is a common medical condition that deprives many individuals worldwide of their normal routine activities. In the absence of external biomarkers, diagnosis of LBP is quite challenging. It requires dealing with several clinical variables, which have no precisely quantified values. Aiming at the development of a fuzzy medical expert system for LBP management, this research proposes an attractive lattice-based knowledge representation scheme for handling imprecision in knowledge, offering a suitable design methodology for a fuzzy knowledge base and a fuzzy inference system. The fuzzy knowledge base is constructed in modular fashion, with each module capturing interrelated medical knowledge about the relevant clinical history, clinical examinations and laboratory investigation results. This approach in design ensures optimality, consistency and preciseness in the knowledge base and scalability. The fuzzy inference system, which uses the Mamdani method, adopts the triangular membership function for fuzzification and the Centroid of Area technique for defuzzification. A prototype of this system has been built using the knowledge extracted from the domain expert physicians. The inference of the system against a few available patient records at the ESI Hospital, Sealdah has been checked. It was found to be acceptable by the verifying medical experts.