Interpretable Artificial Intelligence in Cardiovascular Health: An In-depth Analysis of Heart Disease Data

Abstract

With the escalating abundance of structured and unstructured data and the rapid advancements in analytical techniques, Artificial Intelligence (AI) is catalyzing a revolution in the healthcare industry. However, as AI becomes increasingly indispensable in healthcare, concerns are mounting regarding the lack of transparency, explainability, and potential bias in model predictions. Addressing these issues, Explainable Artificial Intelligence (XAI) emerges as a pivotal solution. XAI plays a crucial role in fostering trust among medical practitioners and AI researchers, thereby paving the way for the broader integration of AI in healthcare. This paper aims to introduce diverse interpretability techniques, shedding light on the comprehensibility and interpretability of XAI systems. These techniques, when applied judiciously, offer significant advantages in the healthcare domain. Given that medical diagnosis models directly impact human life, it is imperative to instill confidence in treating patients based on instructionsfrom seemingly opaque models. The content of this paper includes illustrations grounded in the heart disease dataset, demonstrating how explainability techniques should be prioritized to establish trustworthiness when utilizing AI systems in healthcare. Keywords: Explainable AI, Healthcare, Heart disease, Programming frame- works, LIME, SHAP, Example-based Techniques, Feature-based Techniques.

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