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GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer’s disease and frontotemporal dementia using genetic algorithms



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García Gutierrez, Fernando and Díaz Álvarez, Josefa and Matías-Guiu Guia, Jordi A. and Pytel, Vanesa and Matías-Guiu Guia, Jorge and Cabrera Martín, María Nieves and Ayala Rodrigo, José L. (2022) GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer’s disease and frontotemporal dementia using genetic algorithms. Medical & Biological Engineering & Computing . ISSN 0140-0118

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Official URL: https://doi.org/10.1007/s11517-022-02630-z


Artifcial Intelligence aids early diagnosis and development of new treatments, which is key to slow down the progress of the diseases, which to date have no cure. The patients’ evaluation is carried out through diagnostic techniques such as clinical assessments neuroimaging techniques, which provide high-dimensionality data. In this work, a computational tool is presented that deals with the data provided by the clinical diagnostic techniques. This is a Python-based framework implemented with a modular design and fully extendable. It integrates (i) data processing and management of missing values and outliers; (ii) implementation of an evolutionary feature engineering approach, developed as a Python package, called PyWinEA using Mono-objective and Multi-objetive Genetic Algorithms (NSGAII); (iii) a module for designing predictive models based on a wide range of machine learning algorithms; (iv) a multiclass decision stage based on evolutionary grammars and Bayesian networks. Developed under the eXplainable Artifcial Intelligence and open science perspective, this framework provides promising advances and opens the door to the understanding of neurodegenerative diseases from a data-centric point of view. In this work, we have successfully evaluated the potential of the framework for early and automated diagnosis with neuroimages and neurocognitive assessments from patients with Alzheimer’s disease (AD) and frontotemporal dementia (FTD).

Item Type:Article
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CRUE-CSIC (Acuerdos Transformativos 2022)

Uncontrolled Keywords:Alzheimer’s disease, Frontotemporal dementia, Neurodegenerative diseases, Machine learning, Artificial Intelligence
Subjects:Sciences > Computer science > Artificial intelligence
Medical sciences > Medicine > Neurosciences
ID Code:74111
Deposited On:08 Aug 2022 11:46
Last Modified:08 Aug 2022 11:51

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