Explainable Machine Learning Models for Clinical Decision Support Systems

Explainable Machine Learning (ML) models are an essential component of Clinical Decision Support Systems (CDSS), since they provide the transparency and interpretability that are essential for efficient decision-making in the healthcare industry. With a particular emphasis on their capacity to provide comprehensible explanations in case of clinical predictions along with recommendations, this study investigates the development along with deployment of explainable ML models in (CDSS). We analyze a variety of strategies and methodologies that are used to improve interpretability of ML models in healthcare settings. This is accomplished by doing a substantial study of the relevant literature and case studies. Feature significance analysis, attention mechanisms, rule-based systems, and model-agnostic explanation approaches are some of the techniques that fall under this category. We also examine the difficulties and possibilities that arise when implementing explainable machine learning models in clinical settings that are based on the real world. These include issues around data privacy, compliance with legal requirements, and integration with preexisting processes in the healthcare industry. The purpose of this paper is to educate healthcare professionals, researchers, and policymakers about the potential of transparent and interpretable artificial intelligence models to improve patient outcomes, reduce medical errors, and enhance trust in AI-driven healthcare systems. This will be accomplished by elucidating the benefits and limitations of explainable ML in CDSS.