The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳s disease
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
Parkinson's disease: clinical features and diagnosis
Jankovic, J. (2008) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.2007.131045 · Cited by 5,720 · cited 1×
“Patients with PD are usually characterized by five symptoms including tremor, rigidity, bradykinesia or slowness of movement, hand asymmetry and posture instability [5, 6].”
BackgroundEpidemiology of Parkinson's disease
de Lau, L. M., & Breteler, M. M. (2006) · The Lancet Neurology · doi:10.1016/s1474-4422(06)70471-9 · Cited by 4,110
Cited by
Magnetic Nanoparticles in Targeted Drug Delivery: a Review
kianfar, E. (2021) · Journal of Superconductivity and Novel Magnetism · doi:10.1007/s10948-021-05932-9 · Cited by 280
Different performances of machine learning models to classify dysphonic and non-dysphonic voices
Leite, D. R. A., de Moraes, R. M., & Lopes, L. W. (2025) · Journal of Voice · doi:10.1016/j.jvoice.2022.11.001 · Cited by 15
Metadata
- Authors
- Chen, H.-L., Wang, G., Ma, C., Cai, Z.-N., Liu, W.-B., & Wang, S.-J.
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 184
- Pages
- 131-144
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W2213782059
- Cited by
- 256
- References
- 3
- FWCI
- 9.70
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳s disease. https://app.cochat.ai/papers/10.1016/j.neucom.2015.07.138
- The paper
- Chen, H.-L., Wang, G., Ma, C., Cai, Z.-N., Liu, W.-B., & Wang, S.-J. (2016). An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson׳s disease. Neurocomputing. https://doi.org/10.1016/j.neucom.2015.07.138
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
