RT Journal Article
T1 GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer’s disease and frontotemporal dementia using genetic algorithms
A1 García Gutiérrez, Fernando
A1 Díaz Álvarez, Josefa
A1 Matías-Guiu Guia, Jordi A.
A1 Pytel, Vanesa
A1 Matías-Guiu Guía, Jorge
A1 Cabrera Martín, María Nieves
A1 Ayala Rodrigo, José Luis
AB 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).
PB Springer Nature
SN 0140-0118
YR 2022
FD 2022-07-19
LK https://hdl.handle.net/20.500.14352/71883
UL https://hdl.handle.net/20.500.14352/71883
LA eng
NO García Gutiérrez, F., Díaz Álvarez, J., Matías-Guiu Guia, J. A. et al. «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, vol. 60, n.o 9, septiembre de 2022, pp. 2737-56. DOI.org (Crossref), https://doi.org/10.1007/s11517-022-02630-z.
NO CRUE-CSIC (Acuerdos Transformativos 2022)
NO Ministerio de Ciencia, Innovación y Universidades (España)
NO Instituto de Salud Carlos III/Fondo Europeo de Desarrollo Regional
NO Junta de Extremadura
DS Docta Complutense
RD 20 abr 2025