Cardiovascular disease is an important cause of global morbidity and mortality. Therefore, characterisation of an individual’s cardiovascular system and risk assessment are essential. The blood pressure signal contains relevant information due to the spread of mechanical waves in the arterial tree. The waveform is then influenced by several factors such as the elasticity of the arteries, and the structure of the arterial system. The detection of significant changes may be associated with the presence of cardiovascular diseases in addition to being influenced by natural aging. In the field of artificial intelligence, many complex algorithms, and potentially adapted to large and large numbers of data, have been developed recently thanks to computing powers. In this thesis work, methods will be proposed to carry out machine learning from a database of patients collected. The aim of this project is to develop machine learning methods to characterise the cardiovascular system of patients, with a model specific to the individual, by exploiting blood pressure, in conjunction with the individual’s data.