Class Imbalance in the Prediction of Dementia from Neuropsychological Data

Class imbalance affects medical diagnosis, as the number of disease cases is often outnumbered. When it is severe, learning algorithms fail to retrieve the rarer classes and common assessment metrics become uninformative. In this work, class imbalance is approached using neuropsychological data, with the aim of differentiating Alzheimer’s Disease (AD) from Mild Cognitive Impairment (MCI) and predicting the conversion from MCI to AD. The effect of the imbalance on four learning algorithms is examined through the application of bagging, Bayes risk minimization and MetaCost. Plain decision trees were always outperformed, indicating susceptibility to the imbalance. The naive Bayes classifier was robust but suffered a bias that was adjusted through risk minimization. This strategy outperformed all other combinations of classifiers and meta-learning/ensemble methods. The tree-augmented naive Bayes classifier also benefited from an adjustment of the decision threshold. In the nearly balanced datasets, it was improved by bagging, suggesting that the tree structure was too strong for the attribute dependencies. Support vector machines were robust, as their plain version achieved good results and was never outperformed.