Overview on How Data Mining Tools May Support Cardiovascular Disease Prediction
|Published in:||Issue 2, (Vol. 4) / 2010|
|Author(s):||SITAR- TAUT Dan- Andrei, SITAR- TAUT Adela- Viviana|
|Abstract.||Terms as knowledge discovery or Knowledge Discovery from Databases (KDD), Data Mining (DM), Artificial Intelligence (AI), Machine Learning (ML), Artificial Neural networks (ANN), decision tables and trees, gain from day to day, an increasing significance in medical data analysis. They permit the identification, evaluation, and quantification of some less visible, intuitively unpredictable, by using generally large sets of data. Cardiology represents an extremely vast and important domain, having multiple and complex social and human implications. These are enough reasons to promote the researches in this area, becoming shortly not just national or European priorities, but also world-level ones. The profound and multiple interwoven relationships among the cardiovascular risk factors and cardiovascular diseases – but still far to be completely discovered or understood – represent a niche for applying IT&C modern and multidisciplinary tools in order to solve the existing knowledge gaps. This paper’s aim is to present, by emphasizing their absolute or relative pros and cons, several opportunities of applying DM tools in cardiology, more precisely in endothelial dysfunction diagnostic and quantification the relationships between these and so-called “classical” cardiovascular risk factors.|
|Keywords:||KDD, Data Mining, Cardiovascular Disease, Cardiovascular Risk Factors, Machine Learning Algorithms, Classifiers|
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