Mousa Khadraa Mohamed, Mousa Farid Ali, Mohamed Helalia Shalabi, Elsawy Manal Mohamed
Community Health Nursing Department, Faculty of Nursing, Cairo University, Cairo, Egypt.
Information Technology Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt.
SAGE Open Nurs. 2023 Jul 5;9:23779608231185873. doi: 10.1177/23779608231185873. eCollection 2023 Jan-Dec.
In Egypt, diabetic foot ulcers markedly contribute to the morbidity and mortality of diabetic patients. Accurately predicting the risk of diabetic foot ulcers could dramatically reduce the enormous burden of amputation.
The aim of this study is to design an artificial intelligence-based artificial neural network and decision tree algorithms for the prediction of diabetic foot ulcers.
A case-control study design was utilized to fulfill the aim of this study. The study was conducted at the National Institute of Diabetes and Endocrine Glands, Cairo University Hospital, Egypt. A purposive sample of 200 patients was included. The tool developed and used by the researchers was a structured interview questionnaire including three parts: Part I: demographic characteristics; Part II: medical data; and Part III: in vivo measurements. Artificial intelligence methods were used to achieve the aim of this study.
The researchers used 19 significant attributes based on medical history and foot images that affect diabetic foot ulcers and then proposed two classifiers to predict the foot ulcer: a feedforward neural network and a decision tree. Finally, the researchers compared the results between the two classifiers, and the experimental results showed that the proposed artificial neural network outperformed a decision tree, achieving an accuracy of 97% in the automated prediction of diabetic foot ulcers.
Artificial intelligence methods can be used to predict diabetic foot ulcers with high accuracy. The proposed technique utilizes two methods to predict the foot ulcer; after evaluating the two methods, the artificial neural network showed a higher improvement in performance than the decision tree algorithm. It is recommended that diabetic outpatient clinics develop health education and follow-up programs to prevent complications from diabetes.
在埃及，糖尿病足溃疡是导致糖尿病患者发病和死亡的重要因素。准确预测糖尿病足溃疡的风险可大幅减轻截肢带来的巨大负担。
本研究旨在设计基于人工智能的人工神经网络和决策树算法，用于预测糖尿病足溃疡。
采用病例对照研究设计以实现本研究目的。该研究在埃及开罗大学医院的国家糖尿病与内分泌腺研究所开展。纳入了200名患者的目标样本。研究人员开发并使用的工具是一份结构化访谈问卷，包括三个部分：第一部分：人口统计学特征；第二部分：医疗数据；第三部分：活体测量。使用人工智能方法来实现本研究目的。
研究人员基于影响糖尿病足溃疡的病史和足部图像使用了19个显著属性，然后提出了两种用于预测足部溃疡的分类器：前馈神经网络和决策树。最后，研究人员比较了两种分类器的结果，实验结果表明所提出的人工神经网络优于决策树，在糖尿病足溃疡的自动预测中准确率达到97%。
人工智能方法可用于高精度预测糖尿病足溃疡。所提出的技术利用两种方法预测足部溃疡；在对这两种方法进行评估后，人工神经网络在性能上比决策树算法有更高的提升。建议糖尿病门诊制定健康教育和随访计划，以预防糖尿病并发症。
