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Machine Learning (ML) has emerged as a promising approach in healthcare,outperforming traditional statistical techniques. However, to establish ML as areliable tool in clinical practice, adherence to best practices regarding datahandling, experimental design, and model evaluation is crucial. This worksummarizes and strictly observes such practices to ensure reproducible andreliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection,which serves as a paradigmatic example of challenging problem in healthcare. Weinvestigate the impact of different data augmentation techniques and modelcomplexity on the overall performance. We consider MRI data from ADNI datasetto address a classification problem employing 3D Convolutional Neural Network(CNN). The experiments are designed to compensate for data scarcity and initialrandom parameters by utilizing cross-validation and multiple training trials.Within this framework, we train 15 predictive models, considering threedifferent data augmentation strategies and five distinct 3D CNN architectures,each varying in the number of convolutional layers. Specifically, theaugmentation strategies are based on affine transformations, such as zoom,shift, and rotation, applied concurrently or separately. The combined effect ofdata augmentation and model complexity leads to a variation in predictionperformance up to 10% of accuracy. When affine transformation are appliedseparately, the model is more accurate, independently from the adoptedarchitecture. For all strategies, the model accuracy followed a concavebehavior at increasing number of convolutional layers, peaking at anintermediate value of layers. The best model (8 CL, (B)) is the most stableacross cross-validation folds and training trials, reaching excellentperformance both on the testing set and on an external test set.

The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection