Identifying patterns in multiple biomarkers to diagnose diabetic foot using an explainable genetic programming-based approach

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.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Diabetes mellitus is a global health problem, and its most debilitating complication, diabetic foot, increases the risk of hospitalization, morbidity, and mortality. This study presents a Genetic Programming-based approach called X-GPC, which provides a global interpretation of the diabetic foot ulcer diagnosis through a mathematical model. It also offers a consultable 3D graph for medical staff to understand patients' situations and make decisions for their healing. Experimental results show that the proposal achieves 100% accuracy in diagnosing diabetic foot, outperforming other state-of-the-art techniques.