Cardiac rehabilitation (CR) is an essential secondary prevention component in the treatment of cardiovascular diseases and one of the most cost- effective clinical interventions. Exercise training (ET) in CR programs (CRP) has unequivocal benefits in the reduction of cardiovascular adverse events, by decreasing the overactivated sympathetic tone. This ET added value can be measured by variables that express autonomic control using indirect (standard) or direct (experimental) methodologies. Direct autonomic assessment (ex. Microneurography) is accurate but unusable in daily practice, whereas standard indirect autonomic assessment using clinical parameters is imprecise, resulting in underprescription to safeguard patient safety, with less benefit to the patients. In this project, we aim to apply Machine Learning models to a set of indirect and direct variables, to make a multivariate correlation analysis and so define a normalization factor for exercise prescription.