Investigation into the Application of Artificial Intelligence Methods to the Analysis of Medical Data

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@MastersThesis{setzkorn:masters,

• author = "Christian Setzkorn",
• title = "Investigation into the Application of Artificial Intelligence Methods to the Analysis of Medical Data",
• school = "Computer Science Department, University of Liverpool",
• year = "2000",
• address = "Peach Street, Liverpool L69 7ZF",
• month = jan,
• email = "C.Setzkorn@csc.liv.ac.uk, CSetzkorn@gmx.net",
• keywords = "genetic algorithms, genetic programming, data mining",
• broken = "http://www.csc.liv.ac.uk/~chris/Thesis_Setzkorn_Online.zip",
• broken = "http://www.csc.liv.ac.uk/~chris/SE.html",
• size = "219 pages",
• abstract = "Two methods from the field of artificial intelligence were implemented and employed on a medical data set, in order to perform data mining. The data set consisted of cases from patients who suffered recurring miscarriage, and the aim was to investigate whether the implemented methods were able to identify previously unknown factors associated with recurrent miscarriage. The first approach used a specific type of artificial neural network - Kohonen's self-organizing map for performing clustering within data sets. By using new cluster detection methods and the visualisation possibilities of the employed programming language Java, and its graphical user interface components Swing, it allows interactively the visualisation of relationships within a data set. The second, relatively unique approach, infers rules from a data set by using the paradigm of genetic programming. The rules consist of an IF-part (antecedent) and a THEN-part (consequent). The system has to be supplied with the consequent and works out antecedents, which describe the sub data set indicated by the consequent within the supplied data set. The antecedents produced take the form of a tree where Boolean operations AND, OR and NOT represent nodes, and Boolean expressions represent the leaves. Boolean expressions can be built from all types of data including free-text and real numbers. This system was also implemented with Java and offers in addition the possibility of knowledge extraction from clusters built by the self-organizing map approach.",
• notes = "My master thesis concerns (apart of other things) rule inference from a medical data set by using GP (Data Mining). The rules consist of an IF-part (antecedent) and a THEN-part (consequent). The system has to be supplied with the consequent and works out antecedents, which describe the sub data set indicated by the consequent within the supplied data set. The antecedents produced take the form of a tree where Boolean operations AND, OR and NOT represent nodes, and Boolean expressions represent the leaves. Boolean expressions can be built from all types of data including free-text and real numbers (AGE<=35, DiseaseXYZ = yes, bloo_valueX = abnormal values). This system was implemented with Java and offers in addition the possibility of knowledge extraction from clusters built by a self-organizing map approach (also implemented during this thesis).",

}

Genetic Programming entries for Christian Setzkorn

Citations