Trustworthy and Reliable AI for Heart Disease Diagnosis: Advancing Ethical and Explainable Healthcare Decision-Making

Abstract

The integration of artificial intelligence (AI) in healthcare decision-making has revolutionised the diagnosis and treatment of many diseases. However, challenges such as model interpretability, data quality, algorithmic bias, and ethical considerations remain a barrier. This paper presents a multi-algorithm approach for heart disease diagnosis that prioritises accuracy, explainability, and ethical AI principles. It also aligns with Explainable Artificial Intelligence (XAI) principles by highlighting ante-hoc transparency through careful feature selection and a tailored CNN model design for heart disease diagnosis. By leveraging interpretable AI techniques and addressing key challenges, this paper demonstrates how trustworthy and reliable AI systems can transform healthcare. Additionally, it explores the potential of post-hoc explainability techniques, such as SHAP and LIME, to clarify the model decisions and build trust among the healthcare professionals. This work bridges the gap between AI and the clinical practice.

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