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The motivation of this work is the use of non-invasive and low costtechniques to obtain a faster and more accurate diagnosis of systemic sclerosis(SSc), rheumatic, autoimmune, chronic and rare disease. The technique inquestion is Near Infrared Spectroscopy (NIRS). Spectra were acquired from threedifferent regions of hand's volunteers. Machine learning algorithms are used toclassify and search for the best optical wavelength. The results demonstratethat it is easy to obtain wavelength bands more important for the diagnosis. Weuse the algorithm RFECV and SVC. The results suggests that the most importantwavelength band is at 1270 nm, referring to the luminescence of Singlet Oxygen.The results indicates that the Proximal Interphalangeal Joints region returnsbetter accuracy's scores. Optical spectrometers can be found at low prices andcan be easily used in clinical evaluations, while the algorithms used arecompletely diffused on open source platforms.

Using Near Infrared Spectroscopy and Machine Learning to diagnose Systemic Sclerosis