Risk Analysis of Disease Detection based on DRF algorithm - B., Gomathy;S. M., Ramesh;A., Shamnugam
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Data mining is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from large information repositories such as relational database, data warehouses, XML repository, etc.The detection of most daunting diseases in the earlier stage consistently maximizes the survival rate of the patients. Research work is being activated abundantly for the consistent data analysis to diagnose these threatening ailments by exploiting contrary data mining algorithms. An automatic algorithm for determining frightening diseases for any…mehr

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Produktbeschreibung
Data mining is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from large information repositories such as relational database, data warehouses, XML repository, etc.The detection of most daunting diseases in the earlier stage consistently maximizes the survival rate of the patients. Research work is being activated abundantly for the consistent data analysis to diagnose these threatening ailments by exploiting contrary data mining algorithms. An automatic algorithm for determining frightening diseases for any given dataset is elicited using association rules named Discriminative Rule Framing(DRF).The DRF algorithm predicts the survivability of the disease. The DRF algorithm utilizes feature selection for the effective prediction of risk level in a given dataset. In DRF, association rules are used for dimensionality reduction.
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Autorenporträt
Dr.B.Gomathy received B.E(CSE) degree during 1999 and M.E (CSE) degree during 2009. She has completed her Ph.D. in Anna University during 2014 in the area of Data Mining. Currently, she is working as Associate Professor/CSE in Bannari Amman Institute of Technology. She has published 34 articles in National and International Journals.