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uci cvd datasets  (Kaggle Inc)

 
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    Kaggle Inc uci cvd datasets
    Uci Cvd Datasets, 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/result/uci cvd datasets/product/Kaggle Inc
    Average 86 stars, based on 1 article reviews
    uci cvd datasets - by Bioz Stars, 2026-05
    86/100 stars

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    Shows the confusion matrices of XGB, DNN and the hybrid ensemble on the UCI dataset.

    Journal: Healthcare Technology Letters

    Article Title: A Hybrid Ensemble Approach for Early‐Stage Diabetes Detection

    doi: 10.1049/htl2.70060

    Figure Lengend Snippet: Shows the confusion matrices of XGB, DNN and the hybrid ensemble on the UCI dataset.

    Article Snippet: The public UCI dataset from Kaggle, consisting of self‐reported early‐stage diabetes features [ ], was first used for model development.

    Techniques:

    Training dynamics of the models on the UCI dataset. DNN training and validation loss across epochs (left) and XGB (right).

    Journal: Healthcare Technology Letters

    Article Title: A Hybrid Ensemble Approach for Early‐Stage Diabetes Detection

    doi: 10.1049/htl2.70060

    Figure Lengend Snippet: Training dynamics of the models on the UCI dataset. DNN training and validation loss across epochs (left) and XGB (right).

    Article Snippet: The public UCI dataset from Kaggle, consisting of self‐reported early‐stage diabetes features [ ], was first used for model development.

    Techniques: Biomarker Discovery

    Training and validation accuracy curves for the DNN and XGB models on the UCI dataset.

    Journal: Healthcare Technology Letters

    Article Title: A Hybrid Ensemble Approach for Early‐Stage Diabetes Detection

    doi: 10.1049/htl2.70060

    Figure Lengend Snippet: Training and validation accuracy curves for the DNN and XGB models on the UCI dataset.

    Article Snippet: The public UCI dataset from Kaggle, consisting of self‐reported early‐stage diabetes features [ ], was first used for model development.

    Techniques: Biomarker Discovery

    Comparative evaluation of UCI dataset models: ROC curves with AUC (left) and precision recall curves (right) for XGB, DNN and ensemble.

    Journal: Healthcare Technology Letters

    Article Title: A Hybrid Ensemble Approach for Early‐Stage Diabetes Detection

    doi: 10.1049/htl2.70060

    Figure Lengend Snippet: Comparative evaluation of UCI dataset models: ROC curves with AUC (left) and precision recall curves (right) for XGB, DNN and ensemble.

    Article Snippet: The public UCI dataset from Kaggle, consisting of self‐reported early‐stage diabetes features [ ], was first used for model development.

    Techniques: