Confusion matrix—XGBoost.

<div><p>Background</p><p>Chronic disease monitoring programs often adopt a one-size-fits-all approach that does not consider variation in need, potentially leading to excessive or insufficient support for patients at different risk levels. Machine learning (ML) developments o...

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Bibliographic Details
Main Author:	Sasja Maria Pedersen (11880264) (author)
Other Authors:	Nicolai Damslund (20721181) (author), Trine Kjær (20721184) (author), Kim Rose Olsen (11880270) (author)
Published:	2025
Subjects:	Medicine Biotechnology Cancer Infectious Diseases Mathematical Sciences not elsewhere classified type 2 diabetes tailored disease management monitoring blood glucose guide resource allocation extreme gradient boosting evaluated using roc diverse patient populations different durations associated predicting longer intervals national clinical databases level clinical measures shap model explanations personalised diabetes management test &# 8221 optimising test intervals model &# 8217 find significant potential data contains patient auc scores ranging different risk levels optimal testing interval test interval optimal intervals &# 8220 performing model education levels optimal ), clinical validation xlink "> testing frequencies t2d ). study aims sizeable risk reduce overfitting random forest potentially leading population consists personalised medicine model performance machine learning logistic regression insufficient support healthcare utilisation free resources four categories feature importance determined based consider variation assisting physicians 12 months
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Confusion matrix—XGBoost.

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