Knowledge Engineering Approaches in Medical Diagnostics

Abstract

Medical diagnostics is one of the most critical domains where intelligent systems can significantly improve decision-making accuracy and reduce human errors. Knowledge Engineering (KE) plays a central role in developing computer-based diagnostic systems by capturing, structuring, and applying expert medical knowledge. Over the past few decades, various knowledge engineering approaches such as rule-based systems, case-based reasoning, ontology-driven models, fuzzy systems, Bayesian networks, and hybrid intelligent systems have been applied in healthcare diagnostics. This paper reviews major knowledge engineering techniques used in medical diagnostics and discusses their architecture, advantages, and limitations. It also examines the role of knowledge acquisition, knowledge representation, and inference mechanisms in building clinical decision support systems. A comparative analysis of different approaches is provided along with challenges such as uncertainty handling, knowledge validation, and system scalability. The study concludes that hybrid and ontology-supported approaches are becoming more practical in modern healthcare environments.

Keywords: Knowledge Engineering, Medical Diagnostics, Expert Systems, Clinical Decision Support Systems, Ontologies, Bayesian Networks, Fuzzy Logic, Case-Based Reasoning