ArticleFrontiers in neuroinformatics2018
Robust Ensemble Classification Methodology for I123-Ioflupane SPECT Images and Multiple Heterogeneous Biomarkers in the Diagnosis of Parkinson's Disease.
Article in Frontiers in neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Writing the Future: Artificial Intelligence, Handwriting, and Early Biomarkers for Parkinson's Disease Diagnosis and Monitoring.Biomedicines · 2025Review
- Attention-based Transformer-LSTM architecture for early diagnosis and staging of early-stage Parkinson's disease using fNIRS data.Frontiers in aging neuroscience · 2025Article
- Exploring the Potential Imaging Biomarkers for Parkinson's Disease Using Machine Learning Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Bayesian Optimization with Support Vector Machine Model for Parkinson Disease Classification.Sensors (Basel, Switzerland) · 2023Article
- Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions.Frontiers in aging neuroscience · 2023Review
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- An Ensemble of CNN Models for Parkinson's Disease Detection Using DaTscan Images.Diagnostics (Basel, Switzerland) · 2022Article
- Machine Learning for Early Parkinson's Disease Identification within SWEDD Group Using Clinical and DaTSCAN SPECT Imaging Features.Journal of imaging · 2022Article
- Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation.An international journal on information fusion · 2020Article
- [Molecular imaging and biology · 2020Article
- MRI-Based Radiomics of Basal Nuclei in Differentiating Idiopathic Parkinson's Disease From Parkinsonian Variants of Multiple System Atrophy: A Susceptibility-Weighted Imaging Study.Frontiers in aging neuroscience · 2020Article
- Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatalJournal of neurology · 2019Article
- The Challenge and Opportunity to Diagnose Parkinson's Disease in Midlife.Frontiers in neurology · 2019Review
- Radiomic Features of the Nigrosome-1 Region of the Substantia Nigra: Using Quantitative Susceptibility Mapping to Assist the Diagnosis of Idiopathic Parkinson's Disease.Frontiers in aging neuroscience · 2019Article
- Machine learning studies on major brain diseases: 5-year trends of 2014-2018.Japanese journal of radiology · 2019Review
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
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 Progression Markers 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 defined Morphological Features) from PPMI 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.
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