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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: