Optimizing Dementia Diagnosis Through Distance-Correlation Feature Space and Dimensionality Reduction.

Abstract

The reduction of dimensionality in machine learning and artificial intelligence problems constitutes a pivotal element in the simplification of models, significantly enhancing both their performance and execution time. This process enables the generation of results more rapidly while also facilitating the scalability and optimization of systems that rely on such models. Two primary approaches are commonly employed to achieve dimensionality reduction: feature selection-based methods and those grounded in feature extraction. In this paper, we propose a distance-correlation feature space, upon which we define a dimensionality reduction algorithm based on space transformations and graph embeddings. This methodology is applied in the context of dementia diagnosis through learning models, with the overarching objective of optimizing the diagnostic process.