Table 5_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.xlsx

Table 5_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.xlsx

Objective<p>To develop a self-reportable risk assessment tool for elderly type 2 diabetes mellitus (T2DM) patients, evaluating risks of diabetic nephropathy (DN), retinopathy (DR), peripheral neuropathy (DPN), and diabetic foot (DF) using machine learning, thereby providing new insights and to...

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Main Author: Haochen Liu (487914) (author)
Other Authors: Xiaomiao Li (296609) (author), Ke Shi (154717) (author), Fengyu Lei (20498652) (author), Ziyan Wang (316109) (author), Ziyuan Gao (12567140) (author), Yunxi Liu (520427) (author), Jing Zhu (27724) (author), Jiajia Zhai (22221778) (author), Yi Zhang (9093) (author), Xinyu Li (568185) (author), Shiyu Wang (813972) (author), Yu Niu (785923) (author), Louyan Ma (11041842) (author), Tianxiao Zhang (172364) (author)
Published: 2025
Subjects:
Cell Metabolism
Type 2 diabetes mellitus (T2DM)
diabetic complications
SHAP (Shapley Additive explanation)
machine learning
risk factors
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