@misc{10481/57957,
year = {2018},
month = {8},
url = {http://hdl.handle.net/10481/57957},
abstract = {In last years, several approaches to develop an effective Computer-Aided-Diagnosis
(CAD) system for Parkinson’s Disease (PD) have been proposed. Most of these methods
have focused almost exclusively on brain images through the use of Machine-Learning
algorithms suitable to characterize structural or functional patterns. Those patterns
provide enough information about the status and/or the progression at intermediate
and advanced stages of Parkinson’s Disease. Nevertheless this information could be
insufficient at early stages of the pathology. The Parkinson’s ProgressionMarkers Initiative
(PPMI) database includes neurological images along with multiple biomedical tests.
This information opens up the possibility of comparing different biomarker classification
results. As data come from heterogeneous sources, it is expected that we could include
some of these biomarkers in order to obtain new information about the pathology. Based
on that idea, this work presents an Ensemble Classification model with Performance
Weighting. This proposal has been tested comparing Healthy Control subjects (HC)
vs. patients with PD (considering both PD and SWEDD labeled subjects as the same
class). This model combines several Support-Vector-Machine (SVM) with linear kernel
classifiers for different biomedical group of tests—including CerebroSpinal Fluid (CSF),
RNA, and Serum tests—and pre-processed neuroimages features (Voxels-As-Features
and a list of definedMorphological Features) fromPPMI database subjects. The proposed
methodology makes use of all data sources and selects the most discriminant features
(mainly from neuroimages). Using this performance-weighted ensemble classification
model, classification results up to 96% were obtained.},
organization = {This work was supported by the MINECO/FEDER under
the TEC2015-64718-R project and the Ministry of Economy,
Innovation, Science and Employment of the Junta de Andalucía
under the Excellence Project P11-TIC-7103.},
publisher = {Frontiers Media},
keywords = {Machine learning},
keywords = {Parkinson´s disease},
keywords = {Biomarkers},
title = {Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson’s Disease},
doi = {10.3389/fninf.2018.00053},
author = {Castillo Barnes, Diego and Ramírez Pérez De Inestrosa, Javier and Segovia Román, Fermín and Martínez Murcia, Francisco Jesús and Gorriz Sáez, Juan Manuel},
}