Healthcare systems increasingly encounter patients presenting with multiple chronic diseases, commonly referred to as comorbidities, which complicate diagnosis, treatment planning, and long-term disease management. Conventional clinical decision-making approaches frequently rely on isolated disease-specific guidelines that inadequately address complex interactions among coexisting medical conditions. Consequently, healthcare professionals require intelligent clinical decision support systems (CDSSs) capable of integrating heterogeneous clinical knowledge, patient-specific information, and evidence-based reasoning to facilitate comprehensive decision-making. Recent advances in knowledge engineering, artificial intelligence, decision support technologies, and predictive analytics have significantly improved the capability of healthcare information systems to support clinicians in identifying disease relationships and predicting comorbidity risks. Nevertheless, many existing systems continue to experience limitations related to knowledge representation, interoperability, dynamic clinical reasoning, and adaptive learning.

This research proposes an intelligent knowledge-driven clinical decision support framework designed to improve predictive comorbidity risk assessment while supporting evidence-based healthcare decision-making. The proposed framework integrates structured medical knowledge repositories, ontology-based knowledge management, clinical pathway modeling, predictive analytics, artificial neural networks, decision tree learning, and inference mechanisms within a unified architecture. Rather than functioning solely as a disease prediction model, the framework emphasizes knowledge acquisition, knowledge representation, reasoning, continuous learning, and clinical recommendation generation to assist physicians during diagnosis and treatment planning. The proposed methodology synthesizes established concepts from knowledge management frameworks, expert systems, artificial intelligence, machine learning, clinical guidelines, and predictive modeling reported within the existing literature. Particular emphasis is placed on knowledge operationalization through clinical pathways as advocated by Abidi (2010), thereby enabling systematic management of comorbid disease knowledge throughout the clinical decision process (Abidi, 2010).

The study adopts a comprehensive research and review methodology by critically examining existing knowledge-based decision support models and integrating their strengths into a conceptual framework capable of supporting intelligent healthcare analytics. The framework demonstrates how structured knowledge representation combined with predictive algorithms can improve clinical consistency, reduce diagnostic uncertainty, facilitate personalized healthcare recommendations, and support multidisciplinary decision-making. Analytical findings indicate that combining knowledge-driven reasoning with predictive artificial intelligence enhances decision transparency while maintaining clinical interpretability. Furthermore, the framework illustrates how intelligent healthcare systems may contribute to improved treatment prioritization, optimized resource utilization, and enhanced patient outcomes across complex healthcare environments.

The study contributes to the growing body of research on intelligent healthcare systems by presenting a unified conceptual architecture that bridges knowledge management and predictive clinical analytics. The proposed framework provides theoretical guidance for future implementation of explainable, interoperable, and adaptive clinical decision support systems capable of addressing increasingly complex comorbidity management challenges.