An Approach for Detecting Parkinson’s Disease by Integrating Optimal Feature Selection Strategies with Dense Multiscale Sample Entropy Article Swipe
Minh Tai Pham Nguyen
,
Minh Tran
,
Tadashi Nakano
,
Thi Hong Tran
,
Quoc Duy Nam Nguyen
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/info16010001
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/info16010001
Parkinson’s disease (PD) is a neurological disorder that severely affects motor function, especially gait, requiring accurate diagnosis and assessment instruments. This study presents Dense Multiscale Sample Entropy (DM-SamEn) as an innovative method for diminishing feature dimensions while maintaining the uniqueness of signal features. DM-SamEn employs a weighting mechanism that considers the dynamic properties of the signal, thereby reducing redundancy and improving the distinctiveness of features extracted from vertical ground reaction force (VGRF) signals in patients with Parkinson’s disease. Subsequent to the extraction process, correlation-based feature selection (CFS) and sequential backward selection (SBS) refine feature sets, improving algorithmic accuracy. To validate the feature extraction and selection stage, three classifiers—Adaptive Weighted K-Nearest Neighbors (AW-KNN), Radial Basis Function Support Vector Machine (RBF-SVM), and Multilayer Perceptron (MLP)—were employed to evaluate classification efficacy and ascertain optimal performance across selection strategies, including CFS, SBS, and the hybrid SBS-CFS approach. K-fold cross-validation was employed to provide improved evaluation of model performance by assessing the model on various data subsets, thereby mitigating the risk of overfitting and augmenting the robustness of the results. As a result, the model demonstrated a significant ability to differentiate between PD patients and healthy controls, with classification accuracy reported as ACC [CI 95%: 97.82–98.5%] for disease identification and ACC [CI 95%: 96.3–97.3%] for severity assessment. Optimal performance was primarily achieved through feature sets chosen using SBS and the integrated SBS-CFS methods. The findings highlight the model’s potential as an effective instrument for diagnosing PD and assessing its severity, contributing to advancements in clinical management of the condition.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/info16010001
- https://www.mdpi.com/2078-2489/16/1/1/pdf?version=1735055802
- OA Status
- gold
- Cited By
- 3
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405737984
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405737984Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/info16010001Digital Object Identifier
- Title
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An Approach for Detecting Parkinson’s Disease by Integrating Optimal Feature Selection Strategies with Dense Multiscale Sample EntropyWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-24Full publication date if available
- Authors
-
Minh Tai Pham Nguyen, Minh Tran, Tadashi Nakano, Thi Hong Tran, Quoc Duy Nam NguyenList of authors in order
- Landing page
-
https://doi.org/10.3390/info16010001Publisher landing page
- PDF URL
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https://www.mdpi.com/2078-2489/16/1/1/pdf?version=1735055802Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2078-2489/16/1/1/pdf?version=1735055802Direct OA link when available
- Concepts
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Feature selection, Sample entropy, Artificial intelligence, Computer science, Pattern recognition (psychology), Entropy (arrow of time), Parkinson's disease, Disease, Machine learning, Medicine, Physics, Pathology, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 39 |
| abstract_inverted_index.a | 4, 45, 176, 181 |
| abstract_inverted_index.As | 175 |
| abstract_inverted_index.PD | 187, 240 |
| abstract_inverted_index.To | 98 |
| abstract_inverted_index.an | 29, 235 |
| abstract_inverted_index.as | 28, 196, 234 |
| abstract_inverted_index.by | 154 |
| abstract_inverted_index.in | 73, 248 |
| abstract_inverted_index.is | 3 |
| abstract_inverted_index.of | 40, 53, 63, 151, 166, 172, 251 |
| abstract_inverted_index.on | 158 |
| abstract_inverted_index.to | 79, 124, 147, 184, 246 |
| abstract_inverted_index.ACC | 197, 205 |
| abstract_inverted_index.SBS | 222 |
| abstract_inverted_index.The | 228 |
| abstract_inverted_index.[CI | 198, 206 |
| abstract_inverted_index.and | 17, 59, 87, 103, 119, 128, 138, 168, 189, 204, 223, 241 |
| abstract_inverted_index.for | 32, 201, 209, 238 |
| abstract_inverted_index.its | 243 |
| abstract_inverted_index.the | 38, 50, 54, 61, 80, 100, 139, 156, 164, 170, 173, 178, 224, 231, 252 |
| abstract_inverted_index.was | 145, 214 |
| abstract_inverted_index.(PD) | 2 |
| abstract_inverted_index.95%: | 199, 207 |
| abstract_inverted_index.CFS, | 136 |
| abstract_inverted_index.SBS, | 137 |
| abstract_inverted_index.This | 20 |
| abstract_inverted_index.data | 160 |
| abstract_inverted_index.from | 66 |
| abstract_inverted_index.risk | 165 |
| abstract_inverted_index.sets | 219 |
| abstract_inverted_index.that | 7, 48 |
| abstract_inverted_index.with | 75, 192 |
| abstract_inverted_index.(CFS) | 86 |
| abstract_inverted_index.(SBS) | 91 |
| abstract_inverted_index.Basis | 113 |
| abstract_inverted_index.Dense | 23 |
| abstract_inverted_index.force | 70 |
| abstract_inverted_index.gait, | 13 |
| abstract_inverted_index.model | 152, 157, 179 |
| abstract_inverted_index.motor | 10 |
| abstract_inverted_index.sets, | 94 |
| abstract_inverted_index.study | 21 |
| abstract_inverted_index.three | 106 |
| abstract_inverted_index.using | 221 |
| abstract_inverted_index.while | 36 |
| abstract_inverted_index.(VGRF) | 71 |
| abstract_inverted_index.K-fold | 143 |
| abstract_inverted_index.Radial | 112 |
| abstract_inverted_index.Sample | 25 |
| abstract_inverted_index.Vector | 116 |
| abstract_inverted_index.across | 132 |
| abstract_inverted_index.chosen | 220 |
| abstract_inverted_index.ground | 68 |
| abstract_inverted_index.hybrid | 140 |
| abstract_inverted_index.method | 31 |
| abstract_inverted_index.refine | 92 |
| abstract_inverted_index.signal | 41 |
| abstract_inverted_index.stage, | 105 |
| abstract_inverted_index.Entropy | 26 |
| abstract_inverted_index.Machine | 117 |
| abstract_inverted_index.Optimal | 212 |
| abstract_inverted_index.SBS-CFS | 141, 226 |
| abstract_inverted_index.Support | 115 |
| abstract_inverted_index.ability | 183 |
| abstract_inverted_index.affects | 9 |
| abstract_inverted_index.between | 186 |
| abstract_inverted_index.disease | 1, 202 |
| abstract_inverted_index.dynamic | 51 |
| abstract_inverted_index.employs | 44 |
| abstract_inverted_index.feature | 34, 84, 93, 101, 218 |
| abstract_inverted_index.healthy | 190 |
| abstract_inverted_index.optimal | 130 |
| abstract_inverted_index.provide | 148 |
| abstract_inverted_index.result, | 177 |
| abstract_inverted_index.signal, | 55 |
| abstract_inverted_index.signals | 72 |
| abstract_inverted_index.thereby | 56, 162 |
| abstract_inverted_index.through | 217 |
| abstract_inverted_index.various | 159 |
| abstract_inverted_index.DM-SamEn | 43 |
| abstract_inverted_index.Function | 114 |
| abstract_inverted_index.Weighted | 108 |
| abstract_inverted_index.accuracy | 194 |
| abstract_inverted_index.accurate | 15 |
| abstract_inverted_index.achieved | 216 |
| abstract_inverted_index.backward | 89 |
| abstract_inverted_index.clinical | 249 |
| abstract_inverted_index.disease. | 77 |
| abstract_inverted_index.disorder | 6 |
| abstract_inverted_index.efficacy | 127 |
| abstract_inverted_index.employed | 123, 146 |
| abstract_inverted_index.evaluate | 125 |
| abstract_inverted_index.features | 64 |
| abstract_inverted_index.findings | 229 |
| abstract_inverted_index.improved | 149 |
| abstract_inverted_index.methods. | 227 |
| abstract_inverted_index.patients | 74, 188 |
| abstract_inverted_index.presents | 22 |
| abstract_inverted_index.process, | 82 |
| abstract_inverted_index.reaction | 69 |
| abstract_inverted_index.reducing | 57 |
| abstract_inverted_index.reported | 195 |
| abstract_inverted_index.results. | 174 |
| abstract_inverted_index.severely | 8 |
| abstract_inverted_index.severity | 210 |
| abstract_inverted_index.subsets, | 161 |
| abstract_inverted_index.validate | 99 |
| abstract_inverted_index.vertical | 67 |
| abstract_inverted_index.(AW-KNN), | 111 |
| abstract_inverted_index.K-Nearest | 109 |
| abstract_inverted_index.Neighbors | 110 |
| abstract_inverted_index.accuracy. | 97 |
| abstract_inverted_index.approach. | 142 |
| abstract_inverted_index.ascertain | 129 |
| abstract_inverted_index.assessing | 155, 242 |
| abstract_inverted_index.considers | 49 |
| abstract_inverted_index.controls, | 191 |
| abstract_inverted_index.diagnosis | 16 |
| abstract_inverted_index.effective | 236 |
| abstract_inverted_index.extracted | 65 |
| abstract_inverted_index.features. | 42 |
| abstract_inverted_index.function, | 11 |
| abstract_inverted_index.highlight | 230 |
| abstract_inverted_index.improving | 60, 95 |
| abstract_inverted_index.including | 135 |
| abstract_inverted_index.mechanism | 47 |
| abstract_inverted_index.model’s | 232 |
| abstract_inverted_index.potential | 233 |
| abstract_inverted_index.primarily | 215 |
| abstract_inverted_index.requiring | 14 |
| abstract_inverted_index.selection | 85, 90, 104, 133 |
| abstract_inverted_index.severity, | 244 |
| abstract_inverted_index.weighting | 46 |
| abstract_inverted_index.(DM-SamEn) | 27 |
| abstract_inverted_index.(RBF-SVM), | 118 |
| abstract_inverted_index.Multilayer | 120 |
| abstract_inverted_index.Multiscale | 24 |
| abstract_inverted_index.Perceptron | 121 |
| abstract_inverted_index.Subsequent | 78 |
| abstract_inverted_index.assessment | 18 |
| abstract_inverted_index.augmenting | 169 |
| abstract_inverted_index.condition. | 253 |
| abstract_inverted_index.diagnosing | 239 |
| abstract_inverted_index.dimensions | 35 |
| abstract_inverted_index.especially | 12 |
| abstract_inverted_index.evaluation | 150 |
| abstract_inverted_index.extraction | 81, 102 |
| abstract_inverted_index.innovative | 30 |
| abstract_inverted_index.instrument | 237 |
| abstract_inverted_index.integrated | 225 |
| abstract_inverted_index.management | 250 |
| abstract_inverted_index.mitigating | 163 |
| abstract_inverted_index.properties | 52 |
| abstract_inverted_index.redundancy | 58 |
| abstract_inverted_index.robustness | 171 |
| abstract_inverted_index.sequential | 88 |
| abstract_inverted_index.uniqueness | 39 |
| abstract_inverted_index.algorithmic | 96 |
| abstract_inverted_index.assessment. | 211 |
| abstract_inverted_index.diminishing | 33 |
| abstract_inverted_index.maintaining | 37 |
| abstract_inverted_index.overfitting | 167 |
| abstract_inverted_index.performance | 131, 153, 213 |
| abstract_inverted_index.significant | 182 |
| abstract_inverted_index.strategies, | 134 |
| abstract_inverted_index.(MLP)—were | 122 |
| abstract_inverted_index.advancements | 247 |
| abstract_inverted_index.contributing | 245 |
| abstract_inverted_index.demonstrated | 180 |
| abstract_inverted_index.instruments. | 19 |
| abstract_inverted_index.neurological | 5 |
| abstract_inverted_index.96.3–97.3%] | 208 |
| abstract_inverted_index.Parkinson’s | 0, 76 |
| abstract_inverted_index.differentiate | 185 |
| abstract_inverted_index.97.82–98.5%] | 200 |
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| abstract_inverted_index.identification | 203 |
| abstract_inverted_index.distinctiveness | 62 |
| abstract_inverted_index.cross-validation | 144 |
| abstract_inverted_index.correlation-based | 83 |
| abstract_inverted_index.classifiers—Adaptive | 107 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5073097514 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.84111651 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |