fedesoriano/heart-failure-prediction: papers linking to it

Kaggle · www.kaggle.com/datasets/fedesoriano/heart-failure-prediction

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to fedesoriano/heart-failure-prediction (https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the Kaggle dataset fedesoriano/heart-failure-prediction in their Crossref records, the first in 2023; most of them (1) in 2026.
By category: AI (3); the journals with the most are Expert Systems with Applications (2) and Journal Of Big Data (1).

By year#

20232026

Papers linking to fedesoriano/heart-failure-prediction by publication year
Year	Papers
2023	1
2024	1
2026	1

Journals#

Journals whose papers link to fedesoriano/heart-failure-prediction most
Journal	Papers
Expert Systems with Applications	2
Journal Of Big Data	1

Papers linking to fedesoriano/heart-failure-prediction, most cited first
Published	Title	Journal	Authors	Citations	Link
2023-09-17	Advanced machine learning techniques for cardiovascular disease early detection and diagnosis	Journal Of Big Data	Nadiah Abdulaziz Baghdadi, Sally Mohammed Farghaly Abdelaliem, Amer Malki et al.	178	cites it
2026-01-03	A modified starfish optimization algorithm (M-SFOA) for global optimization problems and its application to heart disease risk prediction	Expert Systems with Applications	Harun Akbulut	43	cites it
2024-06-26	Enhancing healthcare in the digital era: A secure e-health system for heart disease prediction and cloud security	Expert Systems with Applications	Kavitha Vellore Pichandi, Vijayaraj Janarthanan, Tamizhselvi Annamalai et al.	30	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). fedesoriano/heart-failure-prediction: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-fedesoriano-heart-failure-prediction

@misc{scholar4ai-code-kaggle-fedesoriano-heart-failure-prediction,
  author = {{Scholar4AI}},
  title = {fedesoriano/heart-failure-prediction: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-fedesoriano-heart-failure-prediction}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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