The nature of statistical learning theory
The nature of statistical learning theory
Guest Editors‘ Introduction: On Applied Research in MachineLearning
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Ensembles of nested dichotomies for multi-class problems
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Improving the Practice of Classifier Performance Assessment
Neural Computation
Neural Computing and Applications
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines
Artificial Intelligence in Medicine
A review of Bayesian neural networks with an application to near infrared spectroscopy
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Objective: We developed and compared classifiers to predict the outcome of pregnancies of unknown location (PUL). This is a three-class problem, as the possible outcomes are failing PUL, intra-uterine pregnancy (IUP), or ectopic pregnancy (EP). We focused on probabilistic classification because of the importance of uncertainty information in medical decision making. Methods and materials: Nine methods were implemented, based on logistic regression (LR), multi-layer perceptrons, least squares support vector machines (LS-SVMs), and kernel logistic regression (KLR). The LR models involved manual checks for the necessity of input transformations or interaction effects. The classifiers were developed on the training set (n=508) and evaluated on the test set (n=348). We used two performance measures that only evaluate discriminatory potential, and two that investigate the exact probabilities and/or discriminatory potential. Classifier comparison was carried out using a ranking method. Results: The classifier based on a combination of binary LR models using pairwise coupling (LR-PC) ranked first or second for each performance measure. LR-PC obtained test set areas under the receiver operating characteristic curve of 0.989, 0.988, and 0.924 for the prediction of failing PULs, IUPs, and EPs, respectively. Multi-class LR, multi-class KLR, and a combination of binary Bayesian LS-SVMs with radial basis function kernel were always ranked in the top five. Models with low emphasis on nonlinearity were ranked at the bottom. Importantly, LR-PC also performed better than previously developed classifiers based on multi-class LR. Conclusions: The prediction of PULs was good, most notably for failing PULs and IUPs. Logistic regression models performed remarkably well. Multi-class KLR also performed well, whilst nonlinearity was automatically incorporated and probabilistic outputs were directly given without Bayesian analysis or a combination of binary results. The selected inputs are reasonably objective and easy to obtain in clinical practice. Taken together, this study provided useful decision support tools for implementation in clinical practice.