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Kaggle Inc framingham heart disease dataset
Distribution of AUC values for the base model on <t>Framingham</t> dataset.
Framingham Heart Disease Dataset, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/heart+disease+datasets/dataset+framingham/pmc13133109-303-24-31
Average 86 stars, based on 1 article reviews
framingham heart disease dataset - by Bioz Stars, 2026-09
86/100 stars

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1) Product Images from "A cardiovascular disease prediction method based on cross-combination strategy and dynamic weighted stacking ensemble"

Article Title: A cardiovascular disease prediction method based on cross-combination strategy and dynamic weighted stacking ensemble

Journal: Scientific Reports

doi: 10.1038/s41598-026-48006-3

Distribution of AUC values for the base model on Framingham dataset.
Figure Legend Snippet: Distribution of AUC values for the base model on Framingham dataset.

Techniques Used:

ROC curve based on the Framingham dataset.
Figure Legend Snippet: ROC curve based on the Framingham dataset.

Techniques Used:

Feature contribution based on the Framingham dataset.
Figure Legend Snippet: Feature contribution based on the Framingham dataset.

Techniques Used:

Related Articles

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Article Title: Enhancing remote patient monitoring with AI-driven IoMT and cloud computing technologies.
Article Snippet: Kaggle and Statlog’s heart disease datasets, which include 13 clinical factors, have been used to develop the model.

Article Title: An Integrated Two-Layered Voting (TLV) Framework for Coronary Artery Disease Prediction Using Machine Learning Classifiers
Article Snippet: These findings conclude that the TLV model outshined all earlier studies on the severity level classification using two Kaggle’s heart disease datasets.

Article Title: An Integrated Two-Layered Voting (TLV) Framework for Coronary Artery Disease Prediction Using Machine Learning Classifiers
Article Snippet: As a result, we could enhance the results of the proposed model by two-layered hard and soft voting with the hyperparameter tuning on both UCI’s heart disease dataset and Kaggle’s heart disease datasets.

Article Title: Transfer learning-based hybrid VGG16-machine learning approach for heart disease detection with explainable artificial intelligence
Article Snippet: The heart disease datasets from Kaggle comprising 1,025 publicly available and 1,100 synthetic data samples with 14 features were used for this study.

Article Title: Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction.
Article Snippet: We used two heart disease datasets downloaded from Kaggle.

Article Title: Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models
Article Snippet: Two of them are freely available heart disease datasets sourced from Kaggle and the next one is a local dataset collected from medical records of patients who visited Dr. Heshmat Hospital, Guilan, Iran.

Article Title: Performance evaluation of optimal ensemble learning approaches with PCA and LDA-based feature extraction for heart disease prediction
Article Snippet: Heart disease is a global health concern with a high mortality rate, necessitating early, accurate, and reliable prediction methods for effective prevention and control.. In this research, we combine principal component analysis and linear discriminant analysis to reduce dataset complexity and enhance the performance of heart disease classification models by selecting the most relevant features.. We address the class imbalance by employing two balancing techniques: oversampling and the synthetic minority oversampling technique, which ensures a more representative dataset, leading to more accurate predictions.

Article Title: Transfer learning-based hybrid VGG16-machine learning approach for heart disease detection with explainable artificial intelligence
Article Snippet: These two main components were examined over the publicly available Kaggle heart disease datasets with some adjustments and transformations as preprocessing methods.



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Kaggle Inc framingham heart disease dataset
Distribution of AUC values for the base model on <t>Framingham</t> dataset.
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Distribution of AUC values for the base model on Framingham dataset.

Journal: Scientific Reports

Article Title: A cardiovascular disease prediction method based on cross-combination strategy and dynamic weighted stacking ensemble

doi: 10.1038/s41598-026-48006-3

Figure Lengend Snippet: Distribution of AUC values for the base model on Framingham dataset.

Article Snippet: This paper selects three public datasets for research, namely the Cleveland Heart Disease Dataset provided by the University of California Irvine (UCI) database, the Framingham Heart Disease Dataset obtained from the Kaggle platform and the CVD Dataset .

Techniques:

ROC curve based on the Framingham dataset.

Journal: Scientific Reports

Article Title: A cardiovascular disease prediction method based on cross-combination strategy and dynamic weighted stacking ensemble

doi: 10.1038/s41598-026-48006-3

Figure Lengend Snippet: ROC curve based on the Framingham dataset.

Article Snippet: This paper selects three public datasets for research, namely the Cleveland Heart Disease Dataset provided by the University of California Irvine (UCI) database, the Framingham Heart Disease Dataset obtained from the Kaggle platform and the CVD Dataset .

Techniques:

Feature contribution based on the Framingham dataset.

Journal: Scientific Reports

Article Title: A cardiovascular disease prediction method based on cross-combination strategy and dynamic weighted stacking ensemble

doi: 10.1038/s41598-026-48006-3

Figure Lengend Snippet: Feature contribution based on the Framingham dataset.

Article Snippet: This paper selects three public datasets for research, namely the Cleveland Heart Disease Dataset provided by the University of California Irvine (UCI) database, the Framingham Heart Disease Dataset obtained from the Kaggle platform and the CVD Dataset .

Techniques: