Machine Learning Analysis of Retrospective Data From 503 Hospitalized Older Patients With Type 2 Diabetes to Identify Factors Associated With Cognitive Impairment

10 January 2026 : Laboratory Research

Mingzhu Yu^{ABCDEFG 1,2*}, Jianfeng Zhang^{A 1}, Haigeng Chen^{A 3}, Guiyue Li^{A 4}

DOI: 10.12659/MSM.949864

Med Sci Monit 2026; 32:e949864

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Table 2 Model performance metrics after 5-fold cross-validation.

Model	AUC	Accuracy	Sensitivity	Specificity	F1 score
XGBoost	0.892±0.032	0.851±0.028	0.843±0.031	0.859±0.029	0.834±0.033
Random forest	0.863±0.035	0.827±0.030	0.815±0.034	0.839±0.032	0.808±0.035
Logistic regression	0.831±0.038	0.798±0.033	0.782±0.036	0.814±0.034	0.776±0.037
^* Data are shown as mean±standard deviation. XGBoost – eXtreme Gradient Boosting; AUC – area under the curve; SD – standard deviation. ().

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