Key result
Artificial neural networks and fuzzy logic offer potential advantages over traditional statistical methods by better capturing disease complexity and process dynamics in individual cardiovascular risk assessment.
Why the study?
Do artificial intelligence tools improve the accuracy of individual cardiovascular risk assessment compared to classical statistical methods?
Do artificial intelligence tools improve the accuracy of individual cardiovascular risk assessment compared to classical statistical methods?
Artificial intelligence tools offer potential methodological advantages over classical statistical algorithms for capturing the complexity of individualized cardiovascular risk prediction.
Warrants prospective validation trials; leaves open whether neural networks outperform traditional methods in clinical CV risk assessment.
BACKGROUND: In recent years a number of algorithms for cardiovascular risk assessment has been proposed to the medical community. These algorithms consider a number of variables and express their results as the percentage risk of developing a major fatal or non-fatal cardiovascular event in the following 10 to 20 years DISCUSSION: The author has identified three major pitfalls of these algorithms, linked to the limitation of the classical statistical approach in dealing with this kind of non linear and complex information. The pitfalls are the inability to capture the disease complexity, the inability to capture process dynamics, and the wide confidence interval of individual risk assessment. Artificial Intelligence tools can provide potential advantage in trying to overcome these limitations. The theoretical background and some application examples related to artificial neural networks and fuzzy logic have been reviewed and discussed. SUMMARY: The use of predictive algorithms to assess individual absolute risk of cardiovascular future events is currently hampered by methodological and mathematical flaws. The use of newer approaches, such as fuzzy logic and artificial neural networks, linked to artificial intelligence, seems to better address both the challenge of increasing complexity resulting from a correlation between predisposing factors, data on the occurrence of cardiovascular events, and the prediction of future events on an individual level.
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Enzo Grossi (2006) conducted a review in Cardiovascular disease. Artificial neural networks and fuzzy logic vs. Traditional statistical algorithms was evaluated. Artificial neural networks and fuzzy logic offer potential advantages over traditional statistical methods by better capturing disease complexity and process dynamics in individual cardiovascular risk assessment.
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