Diabetes-Prediction-Analysis dataset people distribution of 4 attributes in using the KDE graphs, with green and blue color distributions denoting diabetic (Y) individuals, non-diabetic (N) classes, respectively.

<p>Diabetes-Prediction-Analysis dataset people distribution of 4 attributes in using the KDE graphs, with green and blue color distributions denoting diabetic (Y) individuals, non-diabetic (N) classes, respectively.</p>

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Bibliographic Details
Main Author:	Wangyouchen Zhang (21681025) (author)
Other Authors:	Zhenhua Xia (582939) (author), Guoqing Cai (740684) (author), Junhao Wang (751227) (author), Xutao Dong (21681028) (author)
Published:	2025
Subjects:	Biochemistry Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified typically achieve 95 smaller labeled dataset shapley additive explanations reducing labeling costs quantify feature importance overcome common challenges outperforming traditional models model &# 8217 machine learning models focal active learning fewer labeled samples experimental results demonstrated enhancing model interpretability efficient data utilization called foci ), better prediction outcomes applies attention mechanisms predicting diabetes risk diabetes risk prediction minority class instances method integrates shap imbalanced medical datasets approach significantly improved novel method based diabetes datasets based method minority classes method aims imbalanced classification based sampling xlink "> using similarity study proposes limited generalization iteratively constructed generalization ability evaluation metrics diabetic cases clinical settings
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Diabetes-Prediction-Analysis dataset people distribution of 4 attributes in using the KDE graphs, with green and blue color distributions denoting diabetic (Y) individuals, non-diabetic (N) classes, respectively.

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