Classification of Hemoglobin A1c from Long and Extra-long Term Heart Rate Variability

Abstract

This study classifies Hemoglobin A1c (HbA1c) concentration from long- and extra-long-term heart rate variability (HRV) measurements and various machine learning (ML) models utilizing different datasets. Key metrics include SDNN, RMSSD(NN), NN50, and PNN50 under detailed window-oriented calculations, employing Average, Standard Deviation, and Concatenated methods for feature extraction. A comprehensive pre-processing phase within the ML pipeline ensures analytical robustness. The study systematically conducts patient-wise data splits and evaluates classification performance across various ML models, contributing to a thorough analysis. Evaluation metrics such as sensitivity, specificity, precision, and different F1 scores guide this research in advancing the understanding of HbA1c regulation. The study aspires to establish optimal ML model training and evaluation configurations, contributing to the broader discourse on HbA1c classification. The best-performing model reaches an F1 Score of 93.20%, and F1M of 92.76%, demonstrating its robustness and effectiveness over baseline models.