Authors
Keywords
Abstract
Rough set theory has been widely applied in medical diagnosis to address uncertainty and enhance accuracy. However, most studies overlook identifying critical symptoms, such as those for pneumonia, and fail to propose a decision rule base centered on essential attributes. This research focuses on reducing unnecessary symptoms and identifying core pneumonia indicators using linguistic terms. It also introduces a decision rule base to improve diagnostic precision and support effective decision-making, presenting a promising approach for advancing medical diagnostics.
This research contributes to advancing pneumonia diagnostics by addressing gaps in prior studies that neglect core symptom identification and rely heavily on deterministic methods. By leveraging linguistic terms and rough set theory, the study aims to identify essential symptoms and reduce redundancies. The resulting decision rule base supports accurate, efficient diagnostic processes.
Full Stack Tech Lead, Software Engineering Manager, Principal Engineer – AI Solutions, Senior Solution Architect,Cloud Infrastructure Specialist. Evaluation parameters: Clarity, Market Relevance, Vagueness, Misleading Scope.
The results Full Stack Tech Leadattained the greatest rank, yet Senior Solution Architect it reaches the lowest rank.
“Full Stack Tech Lead has the highest value for Diagnostic Assistance Applications according to the WSM approach”.
Keywords: Medical Diagnosis, Expert Systems, WSM (Weighted Sum Method), Healthcare Technology, Diagnostic Accuracy.
