Özgür Su, Mum Serpilay, Benzer Hilal, Toran Meryem Koçaslan, Toygar İsmail
Translational Pulmonary Research Center-EGESAM, Ege University, Izmir, Turkey.
Institution of Health Sciences, Hatay Mustafa Kemal University, Hatay, Turkey.
Diabetol Metab Syndr. 2024 Oct 7;16(1):244. doi: 10.1186/s13098-024-01480-z.
Foot care self-management is underutilized in older adults and diabetic foot ulcers are more common in older adults. It is important to identify predictors of foot care self-management in older adults with diabetes in order to identify and support vulnerable groups. This study aimed to identify predictors of foot care self-management in older adults with diabetes using a machine learning approach.
This cross-sectional study was conducted between November 2023 and February 2024. The data were collected in the endocrinology and metabolic diseases departments of three hospitals in Turkey. Patient identification form and the Foot Care Scale for Older Diabetics (FCS-OD) were used for data collection. Gradient boosting algorithms were used to predict the variable importance. Three machine learning algorithms were used in the study: XGBoost, LightGBM and Random Forest. The algorithms were used to predict patients with a score below or above the mean FCS-OD score.
XGBoost had the best performance (AUC: 0.7469). The common predictors of the models were age (0.0534), gender (0.0038), perceived health status (0.0218), and treatment regimen (0.0027). The XGBoost model, which had the highest AUC value, also identified income level (0.0055) and A1c (0.0020) as predictors of the FCS-OD score.
The study identified age, gender, perceived health status, treatment regimen, income level and A1c as predictors of foot care self-management in older adults with diabetes. Attention should be given to improving foot care self-management among this vulnerable group.
足部护理自我管理在老年人中未得到充分利用，糖尿病足溃疡在老年人中更为常见。识别老年糖尿病患者足部护理自我管理的预测因素对于识别和支持弱势群体很重要。本研究旨在使用机器学习方法识别老年糖尿病患者足部护理自我管理的预测因素。
本横断面研究于2023年11月至2024年2月进行。数据在土耳其三家医院的内分泌和代谢疾病科室收集。使用患者识别表和老年糖尿病患者足部护理量表（FCS-OD）进行数据收集。使用梯度提升算法预测变量重要性。本研究使用了三种机器学习算法：XGBoost、LightGBM和随机森林。这些算法用于预测FCS-OD评分低于或高于平均值的患者。
XGBoost表现最佳（AUC：0.7469）。模型的常见预测因素为年龄（0.0534）、性别（0.0038）、感知健康状况（0.0218）和治疗方案（0.0027）。AUC值最高的XGBoost模型还将收入水平（0.0055）和糖化血红蛋白（A1c，0.0020）识别为FCS-OD评分的预测因素。
该研究确定年龄、性别、感知健康状况e、治疗方案、收入水平和糖化血红蛋白为老年糖尿病患者足部护理自我管理的预测因素。应关注改善这一弱势群体的足部护理自我管理。
