Machine learning in prognosis of the femoral neck fracture recovery.
We compare the performance of several machine learning algorithms in the problem of prognostics of the femoral neck fracture recovery: the K-nearest neighbours algorithm, the semi-naive Bayesian classifier, backpropagation with weight elimination learning of the multilayered neural networks, the LFC (lookahead feature construction) algorithm, and the Assistant-I and Assistant-R algorithms for top down induction of decision trees using information gain and RELIEFF as search heuristics, respectively. We compare the prognostic accuracy and the explanation ability of different classifiers. Among the different algorithms the semi-naive Bayesian classifier and Assistant-R seem to be the most appropriate. We analyze the combination of decisions of several classifiers for solving prediction problems and show that the combined classifier improves both performance and the explanation ability.
Kukar
M
M
University of Ljubljana, Faculty of Computer and Information Science, Slovenia. (matjaz.kukar.igor.kononenko)@.fer.uni-lj.si
Kononenko
I
I
Silvester
T
T
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D018450:Disease Progression; D005103:Expert Systems; D005265:Femoral Neck Fractures / Q000473:pathology*; D006801:Humans; D008954:Models, Biological; D011379:Prognosis