C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Top 10 algorithms in data mining
Knowledge and Information Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Dietary patterns analysis using data mining method. An application to data from the CYKIDS study
Computer Methods and Programs in Biomedicine
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Objective: To compare the accuracy of a-priori and a-posteriori dietary patterns in the prediction of acute coronary syndrome (ACS) and ischemic stroke. This is actually the first study to employ state-of-the-art classification methods for this purpose. Methods and materials: During 2009-2010, 1000 participants were enrolled; 250 consecutive patients with a first ACS and 250 controls (60+/-12 years, 83% males), as well as 250 consecutive patients with a first stroke and 250 controls (75+/-9 years, 56% males). The controls were population-based and age-sex matched to the patients. The a-priori dietary patterns were derived from the validated MedDietScore, whereas the a-posteriori ones were extracted from principal components analysis. Both approaches were modeled using six classification algorithms: multiple logistic regression (MLR), naive Bayes, decision trees, repeated incremental pruning to produce error reduction (RIPPER), artificial neural networks and support vector machines. The classification accuracy of the resulting models was evaluated using the C-statistic. Results: For the ACS prediction, the C-statistic varied from 0.587 (RIPPER) to 0.807 (MLR) for the a-priori analysis, while for the a-posteriori one, it fluctuated between 0.583 (RIPPER) and 0.827 (MLR). For the stroke prediction, the C-statistic varied from 0.637 (RIPPER) to 0.767 (MLR) for the a-priori analysis, and from 0.617 (decision tree) to 0.780 (MLR) for the a-posteriori. Conclusion: Both dietary pattern approaches achieved equivalent classification accuracy over most classification algorithms. The choice, therefore, depends on the application at hand.