Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold.

Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold.

<p>Comparison of Machine Learning Methods in the Diabetes-Prediction-Analysis dataset. We mark the top three results in bold.</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
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using similarity
study proposes
limited generalization
iteratively constructed
generalization ability
evaluation metrics
diabetic cases
clinical settings
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