This paper explores the burgeoning field of Explainable AI (XAI) within the context of predictive healthcare analytics. With the increasing reliance on machine learning algorithms to make diagnostic and treatment recommendations, the need for XAI becomes paramount. We discuss the significance of transparency and interpretability in AI systems for healthcare, emphasizing the importance of understanding the rationale behind AI predictions. The paper delves into various XAI models that have been developed to enhance the explainability of predictive healthcare analytics, such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and decision trees. We assess the strengths and limitations of these models and their implications for trust, decision-making, and clinical practice. Furthermore, we examine the challenges and opportunities in integrating XAI into existing healthcare workflows and the potential impact on patient outcomes. Ultimately, the paper underscores the necessity of XAI in promoting responsible and ethical use of AI in healthcare to ensure the delivery of high-quality and equitable patient care.
Harris, O. Explainable AI Models for Predictive Healthcare Analytics. Information Sciences and Technological Innovations, 2022, 4, 26. https://doi.org/10.69610/j.isti.20220215
AMA Style
Harris O. Explainable AI Models for Predictive Healthcare Analytics. Information Sciences and Technological Innovations; 2022, 4(1):26. https://doi.org/10.69610/j.isti.20220215
Chicago/Turabian Style
Harris, Olivia 2022. "Explainable AI Models for Predictive Healthcare Analytics" Information Sciences and Technological Innovations 4, no.1:26. https://doi.org/10.69610/j.isti.20220215
APA style
Harris, O. (2022). Explainable AI Models for Predictive Healthcare Analytics. Information Sciences and Technological Innovations, 4(1), 26. https://doi.org/10.69610/j.isti.20220215
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