Cardiovascular disease remains the single most frequent cause of death among people over the age of 65, and it also represents an important and growing share of hospitalization and healthcare costs. Therefore, it is important to evaluate the duration of the hospital stay for patients who are undergoing percutaneous cardiovascular surgery. In this work were considered the data of 1316 patients of the Hospital “A.O.R.N Antonio Cardarelli” of Naples (Italy) who have undergone a surgery on the cardiovascular system percutaneously in the years 2019 and 2020. The dataset has been studied by designing and implementing different Machine Learning (ML) models for improving the effectiveness of our analysis. The results obtained from the analysis are all accurate values, in fact, a precision of more than 75% has always been obtained. In particular, the accuracy values were about 83% for the Random Forest algorithm, while for the Decision Tree algorithm almost 80%. Hence, ML techniques have demonstrated to support further analysis in the medical field.