A method and algorithm for managing decision-making processes for assessing the risk of local vibration disease of the hand and associated neurotic disorders Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.21869/2223-1536-2025-15-3-160-180
The purpose of the research is to improve the quality of risk assessment for vibration-induced stress syndrome (VIS) and associated neurotic disorders in individuals exposed to hand-held vibrating tools by using hybrid models that combine traditional occupational pathology criteria with indicators of the adaptation level of target organs based on fuzzy intelligent technologies. Methods . Given that the class of problems under study is poorly formalized and has an ambiguous description of the data structure, fuzzy decision logic was chosen as the basic mathematical framework, specifically a methodology for synthesizing hybrid fuzzy decision rules. This methodology was used to develop hybrid models for assessing the risk of VIS and associated neurotic disorders. These models, along with traditional modern medical criteria, include indicators characterizing the adaptive potential of the body as a whole and the adaptive potential of target organs. Results . To address the practical challenges of assessing the risk of localized hand vibration disease and associated neurotic disorders, a decision management algorithm was developed for the corresponding decision support system, improving the quality of medical care for the studied patient population. Given the significant use of expert evaluation methods in the synthesis of decision-making models and the general recommendations of the selected methodology, three methods were implemented to validate the performance of the resulting decision rule: expert evaluation, expert modeling of a control sample, and statistical testing on representative control samples. Conclusion . Expert evaluation and expert modeling of control samples revealed that the proposed method enables a 10–20% improvement in the quality of decisions compared to models that do not use indicators characterizing the body's adaptive potential and the level of adaptation of target organs. The same results were obtained during statistical tests conducted according to the rules generally accepted in recognition theory.
Related Topics
- Type
- article
- Landing Page
- https://doi.org/10.21869/2223-1536-2025-15-3-160-180
- https://uprinmatus.elpub.ru/jour/article/download/355/210
- OA Status
- hybrid
- References
- 4
- OpenAlex ID
- https://openalex.org/W7106321702
Raw OpenAlex JSON
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https://openalex.org/W7106321702Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21869/2223-1536-2025-15-3-160-180Digital Object Identifier
- Title
-
A method and algorithm for managing decision-making processes for assessing the risk of local vibration disease of the hand and associated neurotic disordersWork title
- Type
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articleOpenAlex work type
- Publication year
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2025Year of publication
- Publication date
-
2025-11-22Full publication date if available
- Authors
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O. A. Knysh, K. V. Razumova, S. N. Rodionova, S A Filist, V. N. MishustinList of authors in order
- Landing page
-
https://doi.org/10.21869/2223-1536-2025-15-3-160-180Publisher landing page
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https://uprinmatus.elpub.ru/jour/article/download/355/210Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://uprinmatus.elpub.ru/jour/article/download/355/210Direct OA link when available
- Concepts
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Expert system, Fuzzy logic, Machine learning, Computer science, Artificial intelligence, Neuroticism, Adaptation (eye), Decision support system, Medical diagnosis, Control (management), Fuzzy control system, Quality (philosophy), Risk analysis (engineering), Risk management, Decision analysis, Decision model, Fuzzy set, Risk assessment, Management science, Data miningTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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4Number of works referenced by this work
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| abstract_inverted_index.of | 2, 10, 41, 45, 59, 71, 106, 126, 136, 146, 150, 174, 186, 193, 200, 212, 221, 239, 254, 272, 274 |
| abstract_inverted_index.on | 49, 228 |
| abstract_inverted_index.to | 6, 25, 98, 208, 257, 287 |
| abstract_inverted_index.The | 0, 277 |
| abstract_inverted_index.VIS | 107 |
| abstract_inverted_index.and | 18, 66, 108, 132, 155, 196, 225, 236, 269 |
| abstract_inverted_index.for | 13, 88, 102, 165, 177 |
| abstract_inverted_index.has | 67 |
| abstract_inverted_index.not | 261 |
| abstract_inverted_index.the | 3, 8, 42, 57, 72, 81, 104, 123, 127, 133, 143, 148, 166, 172, 178, 183, 191, 197, 201, 210, 213, 244, 252, 265, 270, 288 |
| abstract_inverted_index.use | 185, 262 |
| abstract_inverted_index.was | 78, 96, 163 |
| abstract_inverted_index.This | 94 |
| abstract_inverted_index.body | 128 |
| abstract_inverted_index.care | 176 |
| abstract_inverted_index.data | 73 |
| abstract_inverted_index.hand | 152 |
| abstract_inverted_index.risk | 11, 105, 149 |
| abstract_inverted_index.same | 278 |
| abstract_inverted_index.that | 33, 56, 243, 259 |
| abstract_inverted_index.used | 97 |
| abstract_inverted_index.were | 206, 280 |
| abstract_inverted_index.with | 39, 115 |
| abstract_inverted_index.(VIS) | 17 |
| abstract_inverted_index.Given | 55, 182 |
| abstract_inverted_index.These | 112 |
| abstract_inverted_index.along | 114 |
| abstract_inverted_index.based | 48 |
| abstract_inverted_index.basic | 82 |
| abstract_inverted_index.class | 58 |
| abstract_inverted_index.fuzzy | 50, 75, 91 |
| abstract_inverted_index.level | 44, 271 |
| abstract_inverted_index.logic | 77 |
| abstract_inverted_index.rule: | 216 |
| abstract_inverted_index.rules | 289 |
| abstract_inverted_index.study | 62 |
| abstract_inverted_index.tests | 284 |
| abstract_inverted_index.three | 204 |
| abstract_inverted_index.tools | 28 |
| abstract_inverted_index.under | 61 |
| abstract_inverted_index.using | 30 |
| abstract_inverted_index.whole | 131 |
| abstract_inverted_index.Expert | 234 |
| abstract_inverted_index.body's | 266 |
| abstract_inverted_index.chosen | 79 |
| abstract_inverted_index.during | 282 |
| abstract_inverted_index.expert | 187, 217, 219, 237 |
| abstract_inverted_index.hybrid | 31, 90, 100 |
| abstract_inverted_index.method | 246 |
| abstract_inverted_index.models | 32, 101, 195, 258 |
| abstract_inverted_index.modern | 117 |
| abstract_inverted_index.organs | 47 |
| abstract_inverted_index.poorly | 64 |
| abstract_inverted_index.rules. | 93 |
| abstract_inverted_index.stress | 15 |
| abstract_inverted_index.target | 46, 137, 275 |
| abstract_inverted_index.Methods | 53 |
| abstract_inverted_index.Results | 139 |
| abstract_inverted_index.address | 142 |
| abstract_inverted_index.combine | 34 |
| abstract_inverted_index.control | 223, 230, 240 |
| abstract_inverted_index.develop | 99 |
| abstract_inverted_index.disease | 154 |
| abstract_inverted_index.enables | 247 |
| abstract_inverted_index.exposed | 24 |
| abstract_inverted_index.general | 198 |
| abstract_inverted_index.improve | 7 |
| abstract_inverted_index.include | 120 |
| abstract_inverted_index.medical | 118, 175 |
| abstract_inverted_index.methods | 189, 205 |
| abstract_inverted_index.models, | 113 |
| abstract_inverted_index.organs. | 138, 276 |
| abstract_inverted_index.patient | 180 |
| abstract_inverted_index.purpose | 1 |
| abstract_inverted_index.quality | 9, 173, 253 |
| abstract_inverted_index.results | 279 |
| abstract_inverted_index.sample, | 224 |
| abstract_inverted_index.samples | 241 |
| abstract_inverted_index.studied | 179 |
| abstract_inverted_index.support | 169 |
| abstract_inverted_index.system, | 170 |
| abstract_inverted_index.testing | 227 |
| abstract_inverted_index.theory. | 294 |
| abstract_inverted_index.10–20% | 249 |
| abstract_inverted_index.accepted | 291 |
| abstract_inverted_index.adaptive | 124, 134, 267 |
| abstract_inverted_index.compared | 256 |
| abstract_inverted_index.criteria | 38 |
| abstract_inverted_index.decision | 76, 92, 160, 168, 215 |
| abstract_inverted_index.modeling | 220, 238 |
| abstract_inverted_index.neurotic | 20, 110, 157 |
| abstract_inverted_index.obtained | 281 |
| abstract_inverted_index.problems | 60 |
| abstract_inverted_index.proposed | 245 |
| abstract_inverted_index.research | 4 |
| abstract_inverted_index.revealed | 242 |
| abstract_inverted_index.samples. | 231 |
| abstract_inverted_index.selected | 202 |
| abstract_inverted_index.syndrome | 16 |
| abstract_inverted_index.validate | 209 |
| abstract_inverted_index.according | 286 |
| abstract_inverted_index.algorithm | 162 |
| abstract_inverted_index.ambiguous | 69 |
| abstract_inverted_index.assessing | 103, 147 |
| abstract_inverted_index.conducted | 285 |
| abstract_inverted_index.criteria, | 119 |
| abstract_inverted_index.decisions | 255 |
| abstract_inverted_index.developed | 164 |
| abstract_inverted_index.disorders | 21 |
| abstract_inverted_index.generally | 290 |
| abstract_inverted_index.hand-held | 26 |
| abstract_inverted_index.improving | 171 |
| abstract_inverted_index.localized | 151 |
| abstract_inverted_index.pathology | 37 |
| abstract_inverted_index.potential | 125, 135, 268 |
| abstract_inverted_index.practical | 144 |
| abstract_inverted_index.resulting | 214 |
| abstract_inverted_index.synthesis | 192 |
| abstract_inverted_index.vibrating | 27 |
| abstract_inverted_index.vibration | 153 |
| abstract_inverted_index.Conclusion | 232 |
| abstract_inverted_index.adaptation | 43, 273 |
| abstract_inverted_index.assessment | 12 |
| abstract_inverted_index.associated | 19, 109, 156 |
| abstract_inverted_index.challenges | 145 |
| abstract_inverted_index.disorders, | 158 |
| abstract_inverted_index.disorders. | 111 |
| abstract_inverted_index.evaluation | 188, 235 |
| abstract_inverted_index.formalized | 65 |
| abstract_inverted_index.framework, | 84 |
| abstract_inverted_index.indicators | 40, 121, 263 |
| abstract_inverted_index.management | 161 |
| abstract_inverted_index.structure, | 74 |
| abstract_inverted_index.description | 70 |
| abstract_inverted_index.evaluation, | 218 |
| abstract_inverted_index.implemented | 207 |
| abstract_inverted_index.improvement | 250 |
| abstract_inverted_index.individuals | 23 |
| abstract_inverted_index.intelligent | 51 |
| abstract_inverted_index.methodology | 87, 95 |
| abstract_inverted_index.performance | 211 |
| abstract_inverted_index.population. | 181 |
| abstract_inverted_index.recognition | 293 |
| abstract_inverted_index.significant | 184 |
| abstract_inverted_index.statistical | 226, 283 |
| abstract_inverted_index.traditional | 35, 116 |
| abstract_inverted_index.mathematical | 83 |
| abstract_inverted_index.methodology, | 203 |
| abstract_inverted_index.occupational | 36 |
| abstract_inverted_index.specifically | 85 |
| abstract_inverted_index.synthesizing | 89 |
| abstract_inverted_index.corresponding | 167 |
| abstract_inverted_index.technologies. | 52 |
| abstract_inverted_index.characterizing | 122, 264 |
| abstract_inverted_index.representative | 229 |
| abstract_inverted_index.decision-making | 194 |
| abstract_inverted_index.recommendations | 199 |
| abstract_inverted_index.vibration-induced | 14 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.82587749 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |