Using Near-Infrared Spectroscopy and Machine Learning Algorithms for the Detection of Cardiovascular Diseases and Diabetes Mellitus in Fingernails

The prevalence of cardiovascular diseases (CVDs) and diabetes mellitus (DM) has become a global concern with figures as high as $\mathbf{1 7. 9}$ and $\mathbf{1. 5}$ million lives lost annually [1, 2]. Global figures also suggested that the majority of CVDs and DM are present within low- and middle-income countries (LMICs), where medical equipment, staff and training is limited. As a result, many patients go underdiagnosed or undertreated and instead are left to manifest into further complications such as heart failure or diabetic ketoacidosis, respectively. Therefore, this study aimed to investigate the use of nearinfrared (NIR) spectroscopy paired with machine learning algorithms (MLAs) for detection of CVDs and DM in fingernails. The findings showed key NIR bands related to the glycation of proteins within the fingernails and indicated the presence of disease. Furthermore, binary and multi-class classification models were explored for the classification of healthy, unhealthy, CVD and diabetic fingernails.