Using Near Infrared Spectroscopy and Machine Learning to diagnose Systemic Sclerosis
This work addresses the problem of faster and more accurate diagnosis for patients with systemic sclerosis, an autoimmune disease, using a non-invasive and low-cost technique.
The study tackled diagnosing systemic sclerosis using near-infrared spectroscopy and machine learning, finding that the most important wavelength band for diagnosis is 1270 nm, with the proximal interphalangeal joints region yielding better accuracy scores.
The motivation of this work is the use of non-invasive and low cost techniques to obtain a faster and more accurate diagnosis of systemic sclerosis (SSc), rheumatic, autoimmune, chronic and rare disease. The technique in question is Near Infrared Spectroscopy (NIRS). Spectra were acquired from three different regions of hand's volunteers. Machine learning algorithms are used to classify and search for the best optical wavelength. The results demonstrate that it is easy to obtain wavelength bands more important for the diagnosis. We use the algorithm RFECV and SVC. The results suggests that the most important wavelength band is at 1270 nm, referring to the luminescence of Singlet Oxygen. The results indicates that the Proximal Interphalangeal Joints region returns better accuracy's scores. Optical spectrometers can be found at low prices and can be easily used in clinical evaluations, while the algorithms used are completely diffused on open source platforms.