Heart failure patients have become an important challenge for the healthcare system, since they represent a medical, social and economic problem. Early heart failure diagnoses can be very useful to improve patients’ quality of life and to reduce the resources consumption, but they can be complex for the general practitioners. Data mining and machine learning techniques can really help in this field. The aim of this study is to validate some machine learning models to identify heart failure patients, starting from administrative data, and to make them transparent and interpretable. Despite the lack of clinical data, not available in Italy, but the most employed for the identification of heart failure patients, the results are comparable with the state-of-the-art ones and the models outperform the performances already obtained in Tuscany.