DocumentCode :
1954725
Title :
Knowledge Extraction to Mitigate Child Malnutrition in Developing Countries (Sri Lankan Context)
Author :
Ariyadasa, S.N. ; Munasinghe, L.K. ; Senanayake, S.H.D. ; Fernando, Mahendra
Author_Institution :
Sch. of Comput., Univ. of Colombo, Colombo, Sri Lanka
fYear :
2013
fDate :
29-31 Jan. 2013
Firstpage :
321
Lastpage :
326
Abstract :
Child malnutrition condition can be considered as a major health issue for a country since the children are the future workforce which directly affects the economic growth of the country. This impact is significant mainly in developing countries. This research is carried out with the purpose of reducing the prevalence of child malnutrition based on the SriLankan context with the analysis of the children under five years of age. Some hidden factors were extracted with the use of data mining tools and techniques. The design of the project is based on a conceptual framework which was constructed using the literature gathered. Decision tree technique in classification method was used for rule generation. By testing and validating the knowledge, preventive actions can be taken with the help of medical experts to reduce the malnutrition condition among children for applicable countries.
Keywords :
data mining; decision trees; health care; medical computing; medical disorders; pattern classification; Sri Lankan context; child malnutrition mitigation; classification method; data mining tools; decision tree technique; developing countries; economic growth; health issue; knowledge extraction; knowledge testing; knowledge validation; malnutrition condition reduction; medical experts; preventive actions; rule generation; Context; Data mining; Data models; Decision trees; Economics; Educational institutions; Pediatrics; Child Malnutrition; Classification; Data Mining; Sri Lankan Malnutrition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems Modelling & Simulation (ISMS), 2013 4th International Conference on
Conference_Location :
Bangkok
ISSN :
2166-0662
Print_ISBN :
978-1-4673-5653-4
Type :
conf
DOI :
10.1109/ISMS.2013.23
Filename :
6498288
Link To Document :
بازگشت