# Improvement of a Prediction Model for Heart Failure Survival through Explainable Artificial Intelligence

arXiv: https://arxiv.org/abs/2108.10717 · HTML: https://mlsanity.com/paper/2108.10717/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 94 references that are in the map.
- First posted on arXiv 20 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Interpretability of machine learning based prediction models in healthcare](https://mlsanity.com/paper/2002.08596/) (2020)
- [Automating biomedical data science through tree-based pipeline optimization](https://mlsanity.com/paper/1601.07925/) (2016)
- [Hide-and-Seek: A Template for Explainable AI](https://mlsanity.com/paper/2005.00130/) (2020)
- [Development and evaluation of an Explainable Prediction Model for Chronic Kidney Disease Patients based on Ensemble Trees](https://mlsanity.com/paper/2105.10368/) (2021)

## Built on by (most cited first, 6 of 6)

- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [CardioTabNet: A Novel Hybrid Transformer Model for Heart Disease Prediction using Tabular Medical Data](https://mlsanity.com/paper/2503.17664/) (2025)
- [A Design Framework for operationalizing Trustworthy Artificial Intelligence in Healthcare: Requirements, Tradeoffs and Challenges for its Clinical Adoption](https://mlsanity.com/paper/2504.19179/) (2025)
- [The Role of Explainable AI in Revolutionizing Human Health Monitoring: A Review](https://mlsanity.com/paper/2409.07347/) (2024)
- [When Machine Learning Gets Personal: Evaluating Prediction and Explanation](https://mlsanity.com/paper/2502.02786/) (2025)
- [A Comparative Framework for Evaluating Foundation Models on Tabular Data: A Case Study in Healthcare](https://mlsanity.com/paper/2609.22154/) (2026)

## Abstract (arXiv)

Cardiovascular diseases and their associated disorder of heart failure are one of the major death causes globally, being a priority for doctors to detect and predict its onset and medical consequences. Artificial Intelligence (AI) allows doctors to discover clinical indicators and enhance their diagnosis and treatments. Specifically, explainable AI offers tools to improve the clinical prediction models that experience poor interpretability of their results. This work presents an explainability analysis and evaluation of a prediction model for heart failure survival by using a dataset that comprises 299 patients who suffered heart failure. The model employs a data workflow pipeline able to select the best ensemble tree algorithm as well as the best feature selection technique. Moreover, different post-hoc techniques have been used for the explainability analysis of the model. The paper's main contribution is an explainability-driven approach to select the best prediction model for HF survival based on an accuracy-explainability balance. Therefore, the most balanced explainable prediction model implements an Extra Trees classifier over 5 selected features (follow-up time, serum creatinine, ejection fraction, age and diabetes) out of 12, achieving a balanced-accuracy of 85.1% and 79.5% with cross-validation and new unseen data respectively. The follow-up time is the most influencing feature followed by serum-creatinine and ejection-fraction. The explainable prediction model for HF survival presented in this paper would improve a further adoption of clinical prediction models by providing doctors with intuitions to better understand the reasoning of, usually, black-box AI clinical solutions, and make more reasonable and data-driven decisions.