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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.

Infertility is currently considered an important social problem that has been subject to special interest by medical doctors and biologists. Due to ethical reasons, different legislative restrictions apply in every country on human assisted reproduction techniques such as in-vitro fertilization (IVF). An essential problem in human assisted reproduction is the selection of suitable embryos to transfer in a patient, for which the application of artificial intelligence as well as data mining techniques can be helpful as decision-support systems. In this chapter we introduce a new multi-classification system using Gaussian networks to combine the outputs (probability distributions) of standard machine learning classification algorithms. Our method proposes to consider these outputs as inputs for a superior-level and to apply a stacking scheme to provide a meta-level classification result. We provide a proof of the validity of the approach by employing this multi-classification technique to a complex real medical problem: The selection of the most promising embryo-batch for human in-vitro fertilization treatments.