Predicting special forces dropout via explainable machine learning Article Swipe
Rik Huijzer
,
Peter de Jonge
,
Frank J. Blaauw
,
Maurits Baatenburg de Jong
,
Age de Wit
,
Ruud J. R. Den Hartigh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/ejsc.12162
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/ejsc.12162
Selecting the right individuals for a sports team, organization, or military unit has a large influence on the achievements of the organization. However, the approaches commonly used for selection are either not reporting predictive performance or not explainable (i.e., black box models). In the present study, we introduce a novel approach to selection research, using various machine learning models. We examined 274 special forces recruits, of whom 196 dropped out, who performed a set of physical and psychological tests. On this data, we compared four machine learning models on their predictive performance, explainability, and stability. We found that a stable rule‐based (SIRUS) model was most suitable for classifying dropouts from the special forces selection program. With an averaged area under the curve score of 0.70, this model had good predictive performance, while remaining explainable and stable. Furthermore, we found that both physical and psychological variables were related to dropout. More specifically, a higher score on the 2800 m time, need for connectedness, and skin folds was most strongly associated with dropping out. We discuss how researchers and practitioners can benefit from these insights in sport and performance contexts.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/ejsc.12162
- OA Status
- diamond
- Cited By
- 3
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402813905
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4402813905Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/ejsc.12162Digital Object Identifier
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Predicting special forces dropout via explainable machine learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-24Full publication date if available
- Authors
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Rik Huijzer, Peter de Jonge, Frank J. Blaauw, Maurits Baatenburg de Jong, Age de Wit, Ruud J. R. Den HartighList of authors in order
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https://doi.org/10.1002/ejsc.12162Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://doi.org/10.1002/ejsc.12162Direct OA link when available
- Concepts
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Dropout (neural networks), Machine learning, Artificial intelligence, Social connectedness, Computer science, Stability (learning theory), Selection (genetic algorithm), Set (abstract data type), Special forces, Psychology, Social psychology, Archaeology, History, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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48Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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