iammustafatz/diabetes-prediction-dataset: papers linking to it

Kaggle · www.kaggle.com/datasets/iammustafatz/diabetes-prediction-dataset

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to iammustafatz/diabetes-prediction-dataset (https://www.kaggle.com/datasets/iammustafatz/diabetes-prediction-dataset), 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 iammustafatz/diabetes-prediction-dataset in their Crossref records, the first in 2026; most of them (3) in 2026.
By category: AI (3); the journals with the most are Knowledge and Information Systems (2) and Neural Computing and Applications (1).

By year#

20262026

Papers linking to iammustafatz/diabetes-prediction-dataset by publication year
Year	Papers
2026	3

Journals#

Journals whose papers link to iammustafatz/diabetes-prediction-dataset most
Journal	Papers
Knowledge and Information Systems	2
Neural Computing and Applications	1

Papers linking to iammustafatz/diabetes-prediction-dataset, most cited first
Published	Title	Journal	Authors	Link
2026-01-06	Fed3C: federated clustering-based centralized classification	Knowledge and Information Systems	Emin Akpinar, Bülent Bölat, Murat Taşkıran	cites it
2026-05-20	Mining of kernel concepts with optimization of concept set generation costs	Knowledge and Information Systems	László Kovács, Mohammed Alwersh	cites it
2026-07-01	XCOA-MRFO: a hybrid meta-heuristic framework for simultaneous optimization of architecture and weight of neural networks in medical data classification	Neural Computing and Applications	Maher Talal Alasaady, Teh Noranis Mohd Aris, Nurfadhlina Mohd Sharef et al.	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). iammustafatz/diabetes-prediction-dataset: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-iammustafatz-diabetes-prediction-dataset

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

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