---
title: "sulianova/cardiovascular-disease-dataset: papers citing it"
description: "7 papers in the tracked journals link to the Kaggle dataset sulianova/cardiovascular-disease-dataset in their Crossref records, the first in 2021; most of them (5) in 2025."
url: https://scholar4ai.com/code/kaggle-sulianova-cardiovascular-disease-dataset
markdown_url: https://scholar4ai.com/code/kaggle-sulianova-cardiovascular-disease-dataset.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "7 papers in the tracked journals link to the Kaggle dataset sulianova/cardiovascular-disease-dataset in their Crossref records, the first in 2021; most of them (5) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# sulianova/cardiovascular-disease-dataset: papers linking to it

Research papers in the 480 tracked journals that link to sulianova/cardiovascular-disease-dataset (https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-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.

- Kaggle
- https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset

All repositories: https://scholar4ai.com/code.md

## Key findings

- 7 papers in the tracked journals link to the Kaggle dataset sulianova/cardiovascular-disease-dataset in their Crossref records, the first in 2021; most of them (5) in 2025.
- By category: AI (6) and Operations Research (1); the journals with the most are Knowledge-Based Systems (2), Knowledge and Information Systems (2) and Optimization and Engineering (1).

## By year

*Papers linking to sulianova/cardiovascular-disease-dataset by publication year*

| Year | Papers |
|---|---:|
| 2021 | 1 |
| 2024 | 1 |
| 2025 | 5 |

## Journals

*Journals whose papers link to sulianova/cardiovascular-disease-dataset most*

| Journal | Papers |
|---|---:|
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 2 |
| [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | 2 |
| [Optimization and Engineering](https://scholar4ai.com/journals/optimization-and-engineering.md) | 1 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 1 |
| [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | 1 |

## Papers

*Papers linking to sulianova/cardiovascular-disease-dataset, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2024-07-22 | [An adaptive and late multifusion framework in contextual representation based on evidential deep learning and Dempster–Shafer theory](https://doi.org/10.1007/s10115-024-02150-2) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | Doaa Mohey El-Din, Aboul Ella Hassanein, Ehab E. Hassanien | 13 | cites it |
| 2025-01-25 | [Augmenting Cardiovascular Disease Prediction Through CWCF Integration Leveraging Harris Hawks Search in Deep Belief Networks](https://doi.org/10.1007/s12559-025-10406-4) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | S. Savitha, A. Rajiv Kannan, K. Logeswaran | 13 | cites it |
| 2021-01-06 | [Eigen-stratified models](https://doi.org/10.1007/s11081-020-09592-x) | [Optimization and Engineering](https://scholar4ai.com/journals/optimization-and-engineering.md) | Jonathan Tuck, Stephen Boyd | 5 | cites it |
| 2025-05-12 | [Efficient healthcare data classification through gazelle-inspired optimization and multimodal deep learning](https://doi.org/10.1007/s10115-025-02424-3) | [Knowledge and Information Systems](https://scholar4ai.com/journals/knowledge-and-information-systems.md) | J. Arockia Jaya, K. Mahalakshmi | 4 | cites it |
| 2025-07-28 | [A novel score function for conformal prediction in rule-based binary classification](https://doi.org/10.1016/j.patcog.2025.112219) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Sara Narteni, Alberto Carlevaro, Fabrizio Dabbene et al. | 3 | cites it |
| 2025-08-12 | [Cardiac disease detection utilizing temporal attention recurrent graph convolutional neural network based smart wearable system](https://doi.org/10.1016/j.knosys.2025.114261) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Mr Jibin E P, Dr Menaka D | 1 | cites it |
| 2025-09-07 | [Development of an enhanced heart attack diagnosis model using knowledge distillation and frequent sequence pattern mining](https://doi.org/10.1016/j.knosys.2025.114434) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Dinesh Kumar Bhawnani, Sunita Soni, Arpana Rawal | 1 | 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). sulianova/cardiovascular-disease-dataset: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-sulianova-cardiovascular-disease-dataset

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

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).