Childhood leukaemia relapse risk factors. A rough sets approach.
A rough sets approach was applied to a data set consisting of clinical and laboratory examinations (condition attributes) of children with acute lymphoblastic leukaemia to generate a set of rules for the prediction of disease relapse (conclusion attributes). The information system is presented as a table composed of 69 rows corresponding to the patients and 16 columns corresponding to the attributes. Using manipulation based on rough set theory the information system is reduced to get a subset of a minimum number of attributes ensuring an acceptable quality of classification. Then the conclusion algorithm derived from the reduced system is presented as a conclusion table. The relationship between condition and conclusion attributes is being shown. The research leads to the conclusion that intensive, high dose central nervous system prophylactic irradiation seems to be a better prevention against CNS relapse. Rough set theory is a useful and still complementary tool of medical (biological) data analysis.
Podraza
W
W
Department of Medical Physics, Pomeranian Medical Academy, Szczecin, Poland.
Podraza
H
H
D000293:Adolescent; D017677:Age Distribution; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D015994:Incidence; D007223:Infant; D007256:Information Systems; D008297:Male; D015233:Models, Statistical; D008962:Models, Theoretical; D011044:Poland / Q000453:epidemiology; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000145:classification* / Q000453:epidemiology*; D012008:Recurrence; D012307:Risk Factors; D012680:Sensitivity and Specificity; D017678:Sex Distribution; D013223:Statistics as Topic / Q000145:classification; D015996:Survival Rate