Explainable AI meets Healthcare: A Study on Heart Disease Dataset

TL;DR

This paper explores various explainability techniques for AI models in healthcare, demonstrating how they can enhance trust and transparency in diagnosing heart disease, thereby supporting safer clinical decision-making.

Contribution

It provides a comparative analysis of interpretability methods applied to a heart disease dataset, emphasizing their importance for trustworthy AI in healthcare.

Findings

Explainability techniques improve trust in AI diagnostics.

Interpretability methods help identify model biases.

Enhanced transparency supports clinical decision-making.

Abstract

With the increasing availability of structured and unstructured data and the swift progress of analytical techniques, Artificial Intelligence (AI) is bringing a revolution to the healthcare industry. With the increasingly indispensable role of AI in healthcare, there are growing concerns over the lack of transparency and explainability in addition to potential bias encountered by predictions of the model. This is where Explainable Artificial Intelligence (XAI) comes into the picture. XAI increases the trust placed in an AI system by medical practitioners as well as AI researchers, and thus, eventually, leads to an increasingly widespread deployment of AI in healthcare. In this paper, we present different interpretability techniques. The aim is to enlighten practitioners on the understandability and interpretability of explainable AI systems using a variety of techniques available which can be very advantageous in the health-care domain. Medical diagnosis model is responsible for human life and we need to be confident enough to treat a patient as instructed by a black-box model. Our paper contains examples based on the heart disease dataset and elucidates on how the explainability techniques should be preferred to create trustworthiness while using AI systems in healthcare.