A Predictive Classification Model For Running Injury
| dc.contributor.author | Ganesan, Devesh Raj | |
| dc.date.accessioned | 2022-12-06T01:21:53Z | |
| dc.date.available | 2022-12-06T01:21:53Z | |
| dc.date.issued | 2022-07-25 | |
| dc.description.abstract | Running- related injury is musculoskeletal pain in the lower limbs that causes a restriction on or stoppage of running. Running injuries have been collectively studied in terms of the attributing factors as well as faulty trainings. Various models have been devised to address this issue, however the percentage of running injury occasions are still alarming. Studies have yet to develop a good predictive classification model for running injury. Therefore, the goal of this study was to identify the determinants of running injuries, to classify running data by degree of severity and to develop a predictive classification model of running injury. Two case studies related to running injury were retrieved from the public available domain. Data mining approach was conducted to pre-process and to classify data into three injury levels: low, moderate, and severe risks aided by Waikato Environment for Knowledge Analysis (WEKA) version 3.8.6 tool. The J48, SMO, Random Forest, and Simple Logistic algorithms were used for 10-fold cross validation mode classification benchmarked on the ZeroR baseline algorithm. Findings reveal that classification accuracy obtained were from 70% to 100%. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/16842 | |
| dc.language.iso | en | en_US |
| dc.publisher | Universiti Sains Malaysia | en_US |
| dc.title | A Predictive Classification Model For Running Injury | en_US |
| dc.type | Other | en_US |
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