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REVIEW  ARTIFICIAL INTELLIGENCE IN CARDIOLOGY 

Minerva Cardiology and Angiology 2022 February;70(1):102-22

DOI: 10.23736/S2724-5683.21.05868-3

Copyright © 2021 EDIZIONI MINERVA MEDICA

language: English

Cardiovascular risk prediction: from classical statistical methods to machine learning approaches

Michela SPERTI, Marta MALAVOLTA, Federica STAUNOVO POLACCO, Annalisa DELLAVALLE, Rossella RUGGIERI, Sara BERGIA, Alice FAZIO, Carmine SANTORO, Marco A. DERIU

Department of Mechanical and Aerospace Engineering, PolitoBio MedLab, Polytechnic University of Turin, Turin, Italy



Nowadays, cardiovascular risk prediction scores are commonly used in primary prevention settings. Estimating the cardiovascular individual risk is of crucial importance for effective patient management and optimal therapy identification, with relevant consequences on secondary prevention settings. To reach this goal, a plethora of risk scores have been developed in the past, most of them assuming that each cardiovascular risk factor is linearly dependent on the outcome. However, the overall accuracy of these methods often remains insufficient to solve the problem at hand. In this scenario, machine learning techniques have repeatedly proved successful in improving cardiovascular risk predictions, being able to capture the non-linearity present in the data. In this concern, we present a detailed discussion concerning the application of classical versus machine learning-based cardiovascular risk scores in the clinical setting. This review aimed to give an overview of the current risk scores based on classical statistical approaches and machine learning techniques applied to predict the risk of several cardiovascular diseases, comparing them, discussing their similarities and differences, and highlighting their main drawbacks to aid the physician having a more critical understanding of these tools.


KEY WORDS: Machine learning; Artificial intelligence; Cardiovascular diseases

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