Aprenentatge automàtic per predir risc cardiovascular amb dades clíniques

Abstract

[en] Atherosclerosis is one of the main precursors to cardiovascular pathologies, the first defunction cause on developed countries. One of its principal diagnosis methodologies is carotid ultrasound images due to their low cost and intrusivity. Nonetheless, these produce low quality representations, which makes the diagnosis of atherosclerotic plaques a laborious task. In spite of that, other risk measurement methodologies exist. Risk tables which, taking into consideration diverse lifestyle and medical data, assign the probability of an individual to suffer a cardiovascular event. These types of tables inherit their functionality from the Framingham study, which analyzed data of United States population to create its risk function, thus being the first study to do so. However, adapting these tables to all population is not precise, as there are different epidemiological factors that can affect the values of the tables, and conducting studies to adjust them is expensive. Moreover, other limitations exist, as it has been proved that most of the future cardiovascular events end up classified on mid-range risk groups, thus not being medicated, besides an age limit to apply the tables, and not accepting missing values. This project sets out to improve the current REGICOR risk function, computed in catalan population, using machine learning prediction models and a combination of medical and ultrasound data of volunteers.