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Cardiovascular Risk Prediction in Rheumatic Patients by Artificial Intelligence Paradigms

Michela Sperti

Cardiovascular Risk Prediction in Rheumatic Patients by Artificial Intelligence Paradigms.

Rel. Marco Agostino Deriu, Alberto Audenino. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2019

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According to World Health Organization, cardiovascular diseases are the first cause of death globally. People that present cardiovascular diseases or are at high cardiovascular risk (because of the presence of one or more risk factors such as hypertension, diabetes, hyperlipidemia or already established diseases) need early detection and preventive treatment. In this context, patients affected by inflammatory arthritis present an increased cardiovascular risk. In this field, cardiovascular diseases diagnosis is very tricky even for experts, due to the presence of many concurrent risk factors, some of which uncertain or unknown. Performances of traditional cardiovascular risk algorithms (such as Framingham, CUORE and SCORE) have already been assessed on patients with inflammatory arthritis, but the results show that they tend to underestimate the risk. For this reason, recently, the European League Against Rheumatism recommended to adapt the traditional algorithms with a multiplicative factor of 1.5 in patients with inflammatory arthritis. This work aims at exploring the use of the machine learning techniques to predict cardiovascular risk on patients affected by rheumatic diseases. Machine learning is a subfield of artificial intelligence that introduced a novel paradigm in programming methods. It can be defined as the ability of computers to learn how to solve a given problem without being explicitly programmed for this. In this work several supervised machine learning algorithms were employed to evaluate cardiovascular risk on rheumatic patients. Results of this explorative study open interesting perspectives for future developments of risk predictors.

Relators: Marco Agostino Deriu, Alberto Audenino
Academic year: 2018/19
Publication type: Electronic
Number of Pages: 95
Corso di laurea: Corso di laurea magistrale in Ingegneria Biomedica
Classe di laurea: New organization > Master science > LM-21 - BIOMEDICAL ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/11407
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