Using dynamic Bayesian networks for the prediction of mental deficiency in children with down syndrome

Author

Turki, Houssem ; Ben Ayed, Mounir ; Alimi, Adel M.

Author_Institution

REGIM-Lab., Univ. of Sfax, Sfax, Tunisia

fYear

2014

fDate

11-14 Aug. 2014

Firstpage

163

Lastpage

167

Abstract

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.

Keywords

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; Bayes methods; Classification algorithms; Data mining; Diseases; Educational institutions; Genetics; Pediatrics; Down syndrome; Dynamic Bayesian Network; structure learning; temporal data;

fLanguage

English

Publisher

ieee

Conference_Titel

Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of

Conference_Location

Tunis

Type

conf

DOI

10.1109/SOCPAR.2014.7007999

Filename

7007999