Diabetes mellitus is a global health problem, recognized as the seventh cause of death in the world. One of the most debilitating complications of diabetes mellitus is the diabetic foot (DF), resulting in an increased risk of hospitalization and significant morbidity and mortality. Amputation above or below the knee is a feared complication and the mortality in these patients is higher than for most forms of cancer. Identifying and interpreting relationships existing among the factors involved in DF diagnosis is still challenging. Although machine learning approaches have proven to achieve great accuracy in DF prediction, few advances have been performed in understanding how they make such predictions, resulting in mistrust of their use in real contexts. In this study, we present an approach based on Genetic Programming to build a simple global explainable classifier, named X-GPC, which, unlike existing tools such as LIME and SHAP, provides a global interpretation of the DFU diagnosis through a mathematical model. Also, an easy consultable 3d graph is provided, which could be used by the medical staff to figure out the patients’ situation and take decisions for patients’ healing. Experimental results obtained by using a real-world dataset have shown the ability of the proposal to diagnose DF with an accuracy of 100% outperforming other techniques of the state-of-the-art.

糖尿病是一个全球性的健康问题，被公认为世界第七大死因。糖尿病最使人衰弱的并发症之一是糖尿病足（DF），导致住院风险增加，发病率和死亡率显着增加。膝盖以上或以下的截肢是一种可怕的并发症，这些患者的死亡率高于大多数癌症。识别和解释 DF 诊断所涉及的因素之间存在的关系仍然具有挑战性。尽管机器学习方法已被证明在 DF 预测中实现了极高的准确性，但在理解它们如何进行此类预测方面几乎没有取得进展，导致人们对其在实际环境中的使用不信任。在这项研究中，我们提出了一种基于遗传编程的方法来构建一个简单的全局可解释分类器，称为 X-GPC，与现有的工具（如 LIME 和 SHAP）不同，它通过数学模型提供 DFU 诊断的全局解释。此外，还提供了一个易于查阅的 3d 图形，医务人员可以使用它来了解患者的情况并为患者的康复做出决定。通过使用真实世界数据集获得的实验结果表明，该提议以 100% 的准确度诊断 DF 的能力优于其他最先进的技术。医务人员可以利用它来了解患者的情况并为患者的康复做出决定。通过使用真实世界数据集获得的实验结果表明，该提议以 100% 的准确度诊断 DF 的能力优于其他最先进的技术。医务人员可以利用它来了解患者的情况并为患者的康复做出决定。通过使用真实世界数据集获得的实验结果表明，该提议以 100% 的准确度诊断 DF 的能力优于其他最先进的技术。