Machine learning in prognosis of the femoral neck fracture recovery

Artificial Intelligence in Medicine
M KukarT Silvester

Abstract

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.

Citations

Jul 27, 2001·Artificial Intelligence in Medicine·I Kononenko
Apr 17, 1999·Artificial Intelligence in Medicine·A SeitzD Damm
May 4, 1999·Artificial Intelligence in Medicine·N Lavrac
Aug 4, 2004·Journal of Bioinformatics and Computational Biology·Huiqing Liu, Limsoon Wong
Mar 20, 2008·IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society·Baek Hwan ChoSun I Kim

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