Machine learning in prognosis of the femoral neck fracture recovery. Export

@article{citeulike:6080529, 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.}, author = {Kukar, M. and Kononenko, I. and Silvester, T.}, citeulike-article-id = {6080529}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0933-3657(96)00351-X}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/8955855}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=8955855}, doi = {10.1016/S0933-3657(96)00351-X}, issn = {0933-3657}, journal = {Artificial intelligence in medicine}, keywords = {evaluation, mdds, prognosis}, month = {October}, number = {5}, pages = {431--451}, posted-at = {2009-11-06 11:21:20}, priority = {2}, title = {Machine learning in prognosis of the femoral neck fracture recovery.}, url = {http://dx.doi.org/10.1016/S0933-3657(96)00351-X}, volume = {8}, year = {1996} }

TY - JOUR ID - citeulike:6080529 L3 - citeulike-article-id:6080529 N2 - 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. IS - 5 JF - Artificial intelligence in medicine SN - 0933-3657 EP - 451 TI - Machine learning in prognosis of the femoral neck fracture recovery. VL - 8 SP - 431 KW - evaluation KW - mdds KW - prognosis AU - Kukar, M AU - Kononenko, I AU - Silvester, T PY - 1996/10// UR - http://dx.doi.org/10.1016/S0933-3657(96)00351-X ER -