@misc{10481/84703,
year = {2023},
month = {7},
url = {https://hdl.handle.net/10481/84703},
abstract = {Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data.As shown by our results, the CAD proposal is able to detect PD with 96.48% of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.},
organization = {FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto
	B-TIC-586-UGR20},
organization = {MCIN/AEI
	P20-00525},
organization = {FEDER \Una manerade hacer Europa
	RYC2021-030875-I},
organization = {Junta de Andalucia},
organization = {European Union (EU)
Spanish Government
	RTI2018-098913-B100,
	
	CV20-45250,
	
	A-TIC-080-UGR18},
organization = {European Union (EU)},
organization = {Juan de la Cierva Formacion},
organization = {Next Generation EU Fund through a Margarita Salas Grant},
organization = {Ministerio de Universidades
	FPU18/04902},
publisher = {World Scientific},
keywords = {Ensemble learning},
keywords = {Neuroimaging},
keywords = {Parkinson's disease},
keywords = {MRI},
keywords = {SPECT},
keywords = {Computer-aided-diagnosis},
keywords = {Machine learning},
keywords = {Image processing},
title = {Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data},
doi = {10.1142/S0129065723500417},
author = {Castillo Barnes, Diego and Martínez Murcia, Francisco Jesús and Jiménez Mesa, Carmen and Salas González, Diego and Ramírez Pérez De Inestrosa, Javier and Gorriz Sáez, Juan Manuel},
}