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An Ensemble Method for Diagnosis of Parkinson's Disease Based on Voice Measurements
Sheibani R, Nikookar E, Alavi SE.. Journal of medical signals and sensors, 2019-10-24
Topic: Risk, prevention and care in population studies
Background Parkinson's disease (PD) is the most common destructive neurological disorder after Alzheimer's disease. Unfortunately, there is no specific test such as electroencephalography or blood test for diagnosing the disease. In accordance with the previous studies, about 90% of people with PD have some types of voice abnormalities. Therefore, voice measurements can be used to detect the disease. Methods This study presents an ensemble-based method for identifying patients and healthy samples by class label prediction based on voice frequency characteristics. It includes three stages of data preprocessing, internal classification and ultimate classification. The outcomes of internal classifiers next to primary feature vector of samples are considered the ultimate classifier inputs. Results According to the results, the proposed method achieved 90.6% of accuracy, 95.8% of sensitivity, and 75% of specificity, admissible compared to those of other relevant studies. Conclusion Current experimental outcomes provide a comparative analysis of various machine learning classifiers and confirm that using ensemble-based methods has improved medical diagnostic tasks.
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APA 7th
Sheibani, R., Nikookar, E., & Alavi, S. E. (2019). An Ensemble Method for Diagnosis of Parkinson's Disease Based on Voice Measurements. Journal of medical signals and sensors. https://doi.org/10.4103/jmss.jmss_57_18
Vancouver
Sheibani R, Nikookar E, Alavi SE. An Ensemble Method for Diagnosis of Parkinson's Disease Based on Voice Measurements. Journal of medical signals and sensors. 2019. doi:10.4103/jmss.jmss_57_18. PMID: 31737550.
BibTeX
@article{sheibani2019-31737550,
author = {Sheibani, R. and Nikookar, E. and Alavi, S. E.},
title = {{An Ensemble Method for Diagnosis of Parkinson's Disease Based on Voice Measurements}},
journal = {Journal of medical signals and sensors},
year = {2019},
doi = {10.4103/jmss.jmss_57_18},
pmid = {31737550},
url = {https://doi.org/10.4103/jmss.jmss_57_18}
}
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Key findings
- Results According to the results, the proposed method achieved 90.6% of accuracy, 95.8% of sensitivity, and 75% of specificity, admissible compared to those of other relevant studies.
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Study details
- Effect size90.6%; 95.8%
- Evidence strengthLow
How this study scored 45/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 26 of the 58 points we could judge it on, which is 45 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of medical signals and sensors) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31737550) |
| Free to read | 2 / 2 | Free to read |
| Points | 26 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 26 of the 58 points we could judge it on is 45 out of 100. |
| Total | 45 / 100 | — |
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