Exploring the feasibility of near-infrared spectroscopy and machine learning for detecting cardiovascular diseases and diabetes mellitus in fingernails

Abstract

Cardiovascular diseases (CVDs) and diabetes mellitus (DM) are significant conditions that impact lives around the globe. Frequently employed methods for detecting CVDs and/or DM such as blood work and cardiac catheterisation are often invasive, intrusive and can cause the patient additional physical and psychological harm. Vibrational spectroscopic methods including near-infrared (NIR) spectroscopy have emerged as novel methods for detecting medical conditions and diseases including amyotrophic lateral sclerosis, cancer, DM and periodontitis. NIR spectroscopy's ability to perform rapid and cost-effective analysis saves diagnostic waiting times, providing relief for strained healthcare systems. Moreover, their non-invasive, non-intrusive and non-destructive nature allow application to alternative biological matrices such as hair, fingernails and saliva. Therefore, this work explored the feasibility of NIR spectroscopy paired with machine learning (ML) for detecting CVDs and/or DM in fingernails. NIR spectroscopy successful characterised disease-related spectral features including key NIR regions related to the presence of advanced glycated end-products (AGEs), glycated proteins and DM. To further assess the detective capabilities of NIR spectroscopy, classification models were trained. Cubic and quadratic support vector machine (SVM) models demonstrated accuracy in terms of the classification of healthy, CVD and diabetic fingernails. Accuracy was further improved through binary classification models, which allowed the independent classification of CVD and DM spectra against healthy spectra. In summary, NIR spectroscopy combined with ML provided accurate detection for CVDs and DM in fingernails.