Machine-Learning Techniques for the Diagnosis of COVID-19 from Exhaled-Breath Mass Spectra The emergence of COVID-19 caused by SARS-CoV-2 has created a global health crisis, necessitating rapid and non-invasive diagnostic methods. Traditional approaches like RT-PCR have limitations, so this study aims to use Machine Learning to detect COVID-19 from patients' breath mass spectra. The study began by creating a dataset of mass spectra stored in .ASC files. These files contain multiple acquisitions, each corresponding to a mass spectrum. The first phase aimed to identify the zones where the mass spectrometer is stable, we considered flat the zones with first derivative inside a tolerance guard, then standard deviations within the plateau were computed, and the acquisitions with the lowest values were selected and mass spectra were extracted.