Borhanudin, Mohd Yusof @ Mohamed and Rabiu Muazu, Musa and Mohamad Nizam, Nazarudin and Anwar P. P., Abdul Majeed and Raj, Naresh Bhaskar and Mohd Azraai, Mohd Razman (2024) Development of anthro-fitness model for evaluating firefighter recruits' performance readiness using machine learning. International Journal of Computer Science in Sport, 23 (2). pp. 91-108. ISSN 1684-4769. (Published)
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Abstract
The role of firefighters has evolved from traditional tasks like rescuing cats from trees and extinguishing house fires to more complex land, sea, and air rescues. The increasing demands for public safety necessitate rigorous training and high fitness levels for firefighters to manage their daily tasks effectively. In this study, final assessments of fitness and anthropometric parameters were gathered from 746 Malaysian firefighter recruits. A k-means clustering algorithm was utilized to group the performance levels of the firefighters whilst a quadratic discriminant analysis model was employed to predict the grouping of firefighters based on these parameters. Feature importance analysis was used to identify the most significant parameters contributing to model performance. Concurrently, the Mann-Whitney test was used to determine the essential anthro-fitness parameters differentiating between the groups of firefighters. The k-means clustering identified two performance groups: excellent and average anthro-fitness readiness (EFR and AFR) groups. The model demonstrated a mean performance accuracy of 91% for training and 87% for independent tests. Feature importance analysis revealed that inclined pull-ups, standing broad jump, shuttle run, 2.4 km run, age, and sit-ups were the most significant parameters. The Mann-Whitney test showed that the EFR group outperformed the AFR group in all anthro-fitness parameters except for height, weight, and age, which showed no significant difference. This study highlights the critical role of specific fitness and anthropometric parameters in distinguishing high-performing firefighters. By identifying the most significant contributors to overall fitness, fire departments can better prepare their personnel to meet the increasing public safety demands. The high accuracy of the predictive model also suggests its potential application in ongoing firefighter assessments and training optimization.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Firefighter fitness; Performance assessment; Public safety demands; Quadratic discriminant analysis; Training optimisation |
| Subjects: | T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures |
| Faculty/Division: | Faculty of Manufacturing and Mechatronic Engineering Technology |
| Depositing User: | Mrs. Nurul Hamira Abd Razak |
| Date Deposited: | 30 May 2025 07:58 |
| Last Modified: | 30 May 2025 07:58 |
| URI: | http://umpir.ump.edu.my/id/eprint/44691 |
| Download Statistic: | View Download Statistics |
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