Motor strength classification with machine learning approaches applied to anatomical neuroimages

Pattern recognition methods for classification are leveraged in the field of computational anatomy and neuroimaging showing high reliability and applicability. Body-brain human functions related to the motor-strength features can be discovered by data integration and analysis of 3D brain images, phenotype and behavioural information. This work is focused on the study of feature-based interplay of 3D brain structures with motor-strength information. In particular, this research introduces an ensemble of supervised machine learning approaches for a binary motor-strength classification (strong vs weak) based on 3D brain anatomical features. The proposed approach has been evaluated on 1113 case studies by obtaining well-defined features and reaching the average accuracy of 72% on the test set.