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Your search keyword '"Dan Andrei SITAR TAUT"' showing total 2 results
Start Over Author "Dan Andrei SITAR TAUT" Topic data mining
2 results on '"Dan Andrei SITAR TAUT"'

1. Cardiovascular Attributable Risk and Risk Factors Evaluations as a Matter of Statistics and Data Mining Confluences

Author

Dan Andrei SITAR TAUT and Adela-Viviana SITAR-TAUT

Subjects
Cardiovascular Disease, Myocardial Infarction, Attributable Risk, Roc, Data Mining, Classification, Computer engineering. Computer hardware, TK7885-7895, Bibliography. Library science. Information resources
Abstract

Cardiovascular diseases represent a severe threat for humanity, being the first cause of death and hospitalization in both genders. An impressive number of studies have been developed in order to identify a set of factors causing this kind of illness, but only few of them were able to pay significant resources in analyzing large population samples (tens of thousands) and for longer periods of time (decades). This paper’s objective is to continue the previous researches of the eProCord project and to validate with concrete data the theoretical model developed for the attributable risk (AR). It will consider the same risk factors for myocardial infarction identified by INTERHEART study and the same work hypothesis. We will also evaluate if a certain value of the AR is also confirmed by the invoked disease of the patient. Using statistical and data mining tools we will investigate the prediction potential of the chosen factors and the opportunity to extend them in order to capture any cardiovascular disease. The empirical tests rely for now on a sample of 236 patients.

Published
2010

2. Overview on How Data Mining Tools May Support Cardiovascular Disease Prediction

Author

Dan-Andrei Sitar-Taut and Adela-Viviana Sitar-Taut

Subjects
Machine Learning Algorithms, Cardiovascular Risk Factors, Classifiers, Cardiovascular Disease, lcsh:Mathematics, KDD, Data Mining, lcsh:Electronic computers. Computer science, lcsh:QA1-939, lcsh:QA75.5-76.95
Abstract

Terms as knowledge discovery or KnowledgeDiscovery from Databases (KDD), Data Mining (DM), ArtificialIntelligence (AI), Machine Learning (ML), Artificial Neuralnetworks (ANN), decision tables and trees, gain from day to day,an increasing significance in medical data analysis. They permitthe identification, evaluation, and quantification of some lessvisible, intuitively unpredictable, by using generally large sets ofdata. Cardiology represents an extremely vast and importantdomain, having multiple and complex social and humanimplications. These are enough reasons to promote theresearches in this area, becoming shortly not just national orEuropean priorities, but also world-level ones. The profoundand multiple interwoven relationships among the cardiovascularrisk factors and cardiovascular diseases – but still far to becompletely discovered or understood – represent a niche forapplying IT&C modern and multidisciplinary tools in order tosolve the existing knowledge gaps.This paper’s aim is to present, by emphasizing their absoluteor relative pros and cons, several opportunities of applying DMtools in cardiology, more precisely in endothelial dysfunctiondiagnostic and quantification the relationships between theseand so-called “classical” cardiovascular risk factors.

Published
2010

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