We compare the performance of several machine learning algorithms in t
he problem of prognostics of the femoral neck fracture recovery: the K
-nearest neighbours algorithm, the semi-naive Bayesian classifier, bac
kpropagation with weight elimination learning of the multilayered neur
al networks, the LFC (lookahead feature construction) algorithm, and t
he Assistant-I and Assistant-R algorithms for top down induction of de
cision trees using information gain and RELIEFF as search heuristics,
respectively. We compare the prognostic accuracy and the explanation a
bility of different classifiers. Among the different algorithms the se
mi-naive Bayesian classifier and Assistant-R seem to be the most appro
priate. 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.