Sequential classification approach for enhancing the assessment of cardiac autonomic neuropathy

Abstract

Cardiac autonomic neuropathy (CAN) is a progressive condition associated with chronic diseases like diabetes, requiring regular reviews. Current CAN diagnostic methods are often time-consuming and lack precision. This study presents a novel, two-stage classification model designed to improve CAN diagnostic efficiency. Using a dataset of 1335 patient entries, including inflammatory markers and autonomic function tests (CARTs), the model first classifies patients based on six inflammatory markers– Interleukin-6 (IL-6), C-reactive protein (CRP), Interleukin-1 beta (IL-1beta), Interleukin-10 (IL-10), Monocyte Chemoattractant Protein-1 (MCP-1), and Insulin-like growth factor-1 (IGF-1). In this initial stage, the model achieves 0.893 accuracy for 31.46% of cases in the three-class CAN model at a 0.80 threshold. For cases requiring further assessment, the second stage incorporates CARTs, improving overall accuracy to 0.933. Notably, 98.87% of cases are accurately classified using only a subset of CARTs, with just 1.12% needing all five tests. Additionally, we developed a web application that utilizes Shapley plots to visualize and explain the contribution of each marker, facilitating interpretation for clinical use. This two-stage approach underscores the diagnostic relevance of inflammatory markers, providing clinicians with a streamlined, resource-efficient tool for timely CAN diagnosis and intervention.