• data analysis
• data mining
• liver transplant
• machine learning

Health care industry has always benefited from technological advancement in the field of information technology to improve its service. In recent years with the advent of artificial intelligence and especially machine learning this link is becoming increasingly close as through the use of collected data it is possible to obtain very useful insights. In particular, in this thesis we are going to exploit Data Analytics and Machine Learning in the field of liver transplantation. The purpose of this thesis is divided into two key points. The first is to make available to Italian hospitals that participated in the project a descriptive analysis of the data collected with the help of the Mario Negri Pharmacological Research Institute. This analysis includes several interactive reports to describe the status of liver transplant patients both for all transplant centers involved and at the level of individual centers compared to all the others. The second goal is to use machine learning techniques to predict the hospitalization of liver transplant patients both with the continuous variable "post-transplant hospital stay" and by discretizing it into three main categories: "excellent", "acceptable", and "complicated". Several algorithms were used for the prediction and a comparison between them was made in order to select the best one.