Revealing the Structure of Childhood Abdominal Pain Data and Supporting Diagnostic Decision Making

DOI10.1007/978-3-642-03969-0_16zbMath1186.68455OpenAlexW182233189MaRDI QIDQ3405729

Seferina Mavroudi, G. Anastassopoulos, S. D. Likothanassis, Mirto Ntasi, Adam Adamopoulos, Lazaros Iliadis

Publication date: 11 February 2010

Published in: Engineering Applications of Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-642-03969-0_16

zbMATH Keywords

clusteringgenetic algorithmsdecision treesrandom forestK-meansabdominal painappendicitis

Mathematics Subject Classification ID

Computing methodologies and applications (68U99) Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence (68T35) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Cites Work

• Classification and learning using genetic algorithms. Applications in bioinformatics and web intelligence.
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Revealing the Structure of Childhood Abdominal Pain Data and Supporting Diagnostic Decision Making