ArticleJournal of medical systems2015
Computer-Aided Diagnosis of Parkinson's Disease Using Enhanced Probabilistic Neural Network.
Article in Journal of medical systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The application of electroencephalogram in depression research: bibliometric and technological application analysis from 2005 to 2025.Frontiers in neuroscience · 2025Pooled it
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- EANM practice guideline/SNMMI procedure standard for dopaminergic imaging in Parkinsonian syndromes 1.0.European journal of nuclear medicine and molecular imaging · 2020Guideline
- Probabilistic Neural Networks: An Overview.Methods in molecular biology (Clifton, N.J.) · 2026Review
- Item Response Modeling and Artificial Neural Network for Differentiation of Parkinson's Patients and Subjects Without Evidence of Dopaminergic Deficit.CPT: pharmacometrics & systems pharmacology · 2025Article
- Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.Brain informatics · 2025Review
- Dopaminergic PET to SPECT domain adaptation: a cycle GAN translation approach.European journal of nuclear medicine and molecular imaging · 2025Article
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- Leveraging Computational Intelligence Techniques for Diagnosing Degenerative Nerve Diseases: A Comprehensive Review, Open Challenges, and Future Research Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Explainable machine learning with pairwise interactions for the classification of Parkinson's disease and SWEDD from clinical and imaging features.Brain imaging and behavior · 2022Article
- A major depressive disorder diagnosis approach based on EEG signals using dictionary learning and functional connectivity features.Physical and engineering sciences in medicine · 2022Article
- Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare.Biosensors · 2022Review
- Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson's disease.NPJ Parkinson's disease · 2022Review
- Automatic Diagnosis of Mild Cognitive Impairment Based on Spectral, Functional Connectivity, and Nonlinear EEG-Based Features.Computational and mathematical methods in medicine · 2022Article
- Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).Sensors (Basel, Switzerland) · 2021Review
- Stability Evaluation of Brain Changes in Parkinson's Disease Based on Machine Learning.Frontiers in computational neuroscience · 2021Article
- Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson's disease.Neural computing & applications · 2021Article
- Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements.Medical & biological engineering & computing · 2020Article
- Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network.Journal of medical systems · 2020Article
- Assessment of the Status of Patients with Parkinson's Disease Using Neural Networks and Mobile Phone Sensors.Diagnostics (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early and accurate diagnosis of Parkinson's disease (PD) remains challenging. Neuropathological studies using brain bank specimens have estimated that a large percentages of clinical diagnoses of PD may be incorrect especially in the early stages. In this paper, a comprehensive computer model is presented for the diagnosis of PD based on motor, non-motor, and neuroimaging features using the recently-developed enhanced probabilistic neural network (EPNN). The model is tested for differentiating PD patients from those with scans without evidence of dopaminergic deficit (SWEDDs) using the Parkinson's Progression Markers Initiative (PPMI) database, an observational, multi-center study designed to identify PD biomarkers for diagnosis and disease progression. The results are compared to four other commonly-used machine learning algorithms: the probabilistic neural network (PNN), support vector machine (SVM), k-nearest neighbors (k-NN) algorithm, and classification tree (CT). The EPNN had the highest classification accuracy at 92.5% followed by the PNN (91.6%), k-NN (90.8%) and CT (90.2%). The EPNN exhibited an accuracy of 98.6% when classifying healthy control (HC) versus PD, higher than any previous studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.