# Explainable AI meets Healthcare: A Study on Heart Disease Dataset

arXiv: https://arxiv.org/abs/2011.03195 · HTML: https://mlsanity.com/paper/2011.03195/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [Explainable Artificial Intelligence (XAI) for Internet of Things: A Survey](https://mlsanity.com/paper/2206.04800/) (2022)
- [ECOR: Explainable CLIP for Object Recognition](https://mlsanity.com/paper/2404.12839/) (2024)
- [A Brief Review of Explainable Artificial Intelligence in Healthcare](https://mlsanity.com/paper/2304.01543/) (2023)
- [Local Explanations for Clinical Search Engine results](https://mlsanity.com/paper/2110.12891/) (2021)
- [Reinforcement Explanation Learning](https://mlsanity.com/paper/2111.13406/) (2021)
- [Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges](https://mlsanity.com/paper/2207.04295/) (2022)
- [Two-step interpretable modeling of Intensive Care Acquired Infections](https://mlsanity.com/paper/2301.11146/) (2023)
- [AI-Driven Predictive Analytics Approach for Early Prognosis of Chronic Kidney Disease Using Ensemble Learning and Explainable AI](https://mlsanity.com/paper/2406.06728/) (2024)
- [Explainable AI for Securing Healthcare in IoT-Integrated 6G Wireless Networks](https://mlsanity.com/paper/2505.14659/) (2025)
- [On the Performance of Cyber-Biomedical Features for Intrusion Detection in Healthcare 5.0](https://mlsanity.com/paper/2506.17329/) (2025)

## Abstract (arXiv)

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.