Rough set based lattice structure for knowledge representation in medical expert systems: low back pain management case study
Debarpita Santra, Swapan Kumar Basu, Jyotsna Kumar Mandal, Subrata, Goswami

TL;DR
This paper introduces a rough set based lattice structure for efficient, consistent, and non-redundant knowledge representation in medical expert systems, demonstrated through a low back pain management case study.
Contribution
It proposes a novel lattice-based knowledge representation method that reduces redundancy and inconsistency, improving efficiency and reliability in medical expert systems.
Findings
Reduces processing time and storage requirements.
Generates optimal decision rules with credibility measures.
Ensures completeness, consistency, and non-redundancy.
Abstract
The aim of medical knowledge representation is to capture the detailed domain knowledge in a clinically efficient manner and to offer a reliable resolution with the acquired knowledge. The knowledge base to be used by a medical expert system should allow incremental growth with inclusion of updated knowledge over the time. As knowledge are gathered from a variety of knowledge sources by different knowledge engineers, the problem of redundancy is an important concern here due to increased processing time of knowledge and occupancy of large computational storage to accommodate all the gathered knowledge. Also there may exist many inconsistent knowledge in the knowledge base. In this paper, we have proposed a rough set based lattice structure for knowledge representation in medical expert systems which overcomes the problem of redundancy and inconsistency in knowledge and offers…
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