Neural networks and logistic regression: Part II
Computational Statistics & Data Analysis
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Supervised dimension reduction of intrinsically low-dimensional data
Neural Computation
Knowledge discovery with classification rules in a cardiovascular dataset
Computer Methods and Programs in Biomedicine
Computational Biology and Chemistry
From projection pursuit and CART to adaptive discriminant analysis?
IEEE Transactions on Neural Networks
Review: Data mining techniques and applications - A decade review from 2000 to 2011
Expert Systems with Applications: An International Journal
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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We aim at evaluating how data-mining statistical techniques can be applied on medical records and administrative data of diabetes and how they differ in terms of capabilities of predicting outcomes (e.g. death). Data on 3,892 outpatient patients with a diagnosis of type 2 diabetes from the San Giovanni Battista Hospital in Torino. Six statistical classifiers were applied: Logistic regression (LR), Generalized Additive Model (GAM), Projection pursuit Regression (PPR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Artificial Neural Networks (ANN). All models selected the same subset of covariates. ANN is the model performing worse, whereas simpler models, like LR, GAM and LDA seem to perform better. GAM is associated with a very small misclassification rate. The agreement in predicting individual outcomes among models is 0.23 (SE 0.06, Kappa). Monitoring on the basis of patients' characteristics is highly dependent from the statistical properties of the chosen statistical model.