Using dynamic Bayesian networks for the prediction of mental deficiency in children with down syndrome
Soft Computing and Pattern Recognition（2014）
摘要
This work is located in the domain of the Knowledge Discovery from Data (KDD). The purpose of the KDD is the extraction of knowledge or of a knowledge starting from great number of data which evolve in a dynamic way. In this work we propose an approach for the temporal KDD. The Bayesian Network (BN) is one of the techniques used in KDD. Our objective comes back to fix the best algorithm of incremental learning of structure extracted by the Dynamic Bayesian Network (DBN) and using it in the decision making in a dynamic way. Our scope of application is the case of Down Syndrome (DS) also known as trisomy 21, the data are provided by the medical genetics and Child Psychiatry units of the university hospital Hedi Chaker Sfax, Tunisia.
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关键词
belief networks,data mining,decision making,diseases,genetics,knowledge acquisition,learning (artificial intelligence),paediatrics,psychology,DBN,Hedi Chaker Sfax,Tunisia,child psychiatry units,children with down syndrome,decision making,dynamic Bayesian networks,incremental learning,knowledge discovery from data,knowledge extraction,medical genetics,mental deficiency prediction,structure extraction,temporal KDD,university hospital,Down syndrome,Dynamic Bayesian Network,structure learning,temporal data
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