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Article

An Ensemble Learning Approach Based on Diffusion Tensor Imaging Measures for Alzheimer’s Disease Classification

1
Innovation Lab, Exprivia S.p.A., Via A. Olivetti 11, I-70056 Molfetta, Italy
2
Department of Electrical and Information Engineering (DEI), Politecnico di Bari, Via E. Orabona 4, I-70125 Bari, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Heung-Il Suk
Electronics 2021, 10(3), 249; https://doi.org/10.3390/electronics10030249
Received: 30 December 2020 / Revised: 18 January 2021 / Accepted: 19 January 2021 / Published: 22 January 2021
(This article belongs to the Special Issue Computational Intelligence in Healthcare)
Recent advances in neuroimaging techniques, such as diffusion tensor imaging (DTI), represent a crucial resource for structural brain analysis and allow the identification of alterations related to severe neurodegenerative disorders, such as Alzheimer’s disease (AD). At the same time, machine-learning-based computational tools for early diagnosis and decision support systems are adopted to uncover hidden patterns in data for phenotype stratification and to identify pathological scenarios. In this landscape, ensemble learning approaches, conceived to simulate human behavior in making decisions, are suitable methods in healthcare prediction tasks, generally improving classification performances. In this work, we propose a novel technique for the automatic discrimination between healthy controls and AD patients, using DTI measures as predicting features and a soft-voting ensemble approach for the classification. We show that this approach, efficiently combining single classifiers trained on specific groups of features, is able to improve classification performances with respect to the comprehensive approach of the concatenation of global features (with an increase of up to 9% on average) and the use of individual groups of features (with a notable enhancement in sensitivity of up to 11%). Ultimately, the feature selection phase in similar classification tasks can take advantage of this kind of strategy, allowing one to exploit the information content of data and at the same time reducing the dimensionality of the feature space, and in turn the computational effort. View Full-Text
Keywords: diffusion tensor imaging; ensemble learning; decision support systems; healthcare; machine learning; computational intelligence; Alzheimer’s disease diffusion tensor imaging; ensemble learning; decision support systems; healthcare; machine learning; computational intelligence; Alzheimer’s disease
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MDPI and ACS Style

Lella, E.; Pazienza, A.; Lofù, D.; Anglani, R.; Vitulano, F. An Ensemble Learning Approach Based on Diffusion Tensor Imaging Measures for Alzheimer’s Disease Classification. Electronics 2021, 10, 249. https://doi.org/10.3390/electronics10030249

AMA Style

Lella E, Pazienza A, Lofù D, Anglani R, Vitulano F. An Ensemble Learning Approach Based on Diffusion Tensor Imaging Measures for Alzheimer’s Disease Classification. Electronics. 2021; 10(3):249. https://doi.org/10.3390/electronics10030249

Chicago/Turabian Style

Lella, Eufemia, Andrea Pazienza, Domenico Lofù, Roberto Anglani, and Felice Vitulano. 2021. "An Ensemble Learning Approach Based on Diffusion Tensor Imaging Measures for Alzheimer’s Disease Classification" Electronics 10, no. 3: 249. https://doi.org/10.3390/electronics10030249

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