Symbolic regression and interpretable ensemble learning approach in determining early onset of diabetic peripheral neuropathy

Abstract

The detrimental consequences of diabetic peripheral neuropathy (DPN), a prevalent comorbidity of type 2 diabetic mellitus, include heightened morbidity and mortality, as well as a reduced quality of life. Unfortunately, this condition has been identified on a frequent basis in recent years, but it is either inadequately diagnosed or remains untreated. Diabetic peripheral neuropathy is caused and advances by a complex interplay of metabolic process imbalance, immune system dysfunction, oxidative stress, and endothelial dysfunction each of which has an impact on its multifactorial pathogenesis. Acquiring information on the physiological traits associated with the DPN group can assist in recognizing and explaining the possible development of early warning systems. The key goal of this investigation is to comprehensively examine the physiological traits that differentiate the DPN and No DPN groups. Our present research has resulted in the creation of precise prediction models that can effectively distinguish between individuals with DPN and those without DPN based on physiological characteristics. Additionally, we employed model-agnostic techniques to convert a black box model into a transparent model, allowing us to get insights into the underlying physiological characteristics of the two groups. We also used Qlattice symbolic regression method to develop transparent models exhibiting a non-linear relationship between DPN and the collective effect of Urea and Endocan, which are the two most important potential biomarkers.