Voxel-Based Diagnosis of Alzheimer's Disease Using Classifier Ensembles.

Functional magnetic resonance imaging (fMRI) is one of the most promising noninvasive techniques for early Alzheimer's disease (AD) diagnosis. In this paper, we explore the application of different machine learning techniques to the classification of fMRI data for this purpose. The functional images were first preprocessed using the statistical parametric mapping toolbox to output individual maps of statistically activated voxels. A fast filter was applied afterwards to select voxels commonly activated across demented and nondemented groups. Four feature ranking selection techniques were embedded into a wrapper scheme using an inner-outer loop for the selection of relevant voxels. The wrapper approach was guided by the performance of six pattern recognition models, three of which were ensemble classifiers based on stochastic searches. Final classification performance was assessed from the nested internal and external cross-validation loops taking several voxel sets ordered by importance. Numerical performance was evaluated using statistical tests, and the best combination of voxel selection and classification reached a 97.14% average accuracy. Results repeatedly pointed out Brodmann regions with distinct activation patterns between demented and nondemented profiles, indicating that the machine learning analysis described is a powerful method to detect differences in several brain regions between both groups.

Predicting dementia development in Parkinson's disease using Bayesian network classifiers.

Parkinson's disease (PD) has broadly been associated with mild cognitive impairment (PDMCI) and dementia (PDD). Researchers have studied surrogate, neuroanatomic biomarkers provided by magnetic resonance imaging (MRI) that may help in the early diagnosis of this condition. In this article, four classification models (naïve Bayes, multivariate filter-based naïve Bayes, filter selective naïve Bayes and support vector machines, SVM) have been applied to evaluate their capacity to discriminate between cognitively intact patients with Parkinson's disease (PDCI), PDMCI and PDD. For this purpose, the MRI studies of 45 subjects (16 PDCI, 15 PDMCI and 14 PDD) were acquired and post-processed with Freesurfer, obtaining 112 variables (volumes of subcortical structures and thickness of cortical parcels) per subject. A multivariate filter-based naïve Bayes model was found to be the best classifier, having the highest cross-validated sensitivity, specificity and accuracy. Additionally, the most relevant variables related to dementia in PD, as predicted by our classifiers, were cerebral white matter, and volumes of the lateral ventricles and hippocampi.

Selection of human embryos for transfer by Bayesian classifiers.

In this work we approach by Bayesian classifiers the selection of human embryos from images. This problem consists of choosing the embryos to be transferred in human-assisted reproduction treatments, which Bayesian classifiers address as a supervised classification problem. Different Bayesian classifiers capable of taking into account diverse dependencies between variables of this problem are tested in order to analyse their performance and validity for building a potential decision support system. The analysis by receiver operating characteristic (ROC) proves that the Bayesian classifiers presented in this paper are an appropriated and robust approach for this aim. From the Bayesian classifiers tested, the tree augmented naive Bayes, k-dependence Bayesian and naive Bayes classifiers showed to perform almost as well as the semi naive Bayes and selective naive Bayes classifiers.