Explainable AI in Big Data Analytics for Healthcare Applications

Abstract

Explainable Artificial Intelligence (XAI) emerges as a critical component in Big Data Analytics for healthcare applications. Traditional AI models, particularly deep learning-based systems, operate as black boxes, making it challenging for healthcare professionals to understand their decision-making processes. The integration of XAI in healthcare enables transparency, trust, and interpretability, which are crucial for regulatory compliance and clinical adoption. This paper explores the role of XAI in handling vast and complex healthcare datasets, enhancing predictive analytics, and improving patient outcomes. Various XAI methods such as SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), and attention mechanisms in deep learning are examined. A framework integrating XAI with Big Data Analytics is proposed, demonstrating its efficiency in disease diagnosis and treatment recommendation. Results from experimental evaluations indicate that XAI-driven models significantly enhance decision-making capabilities while maintaining high accuracy. The paper concludes by discussing the challenges and future directions in the development of interpretable AI solutions for healthcare.