Paediatric upper limb fracture healing time prediction using a machine learning approach
To analyse and predict the healing time of upper limb fractures in children, machine learning algorithms were used. Paediatric orthopaedic data was obtained from the University Malaya Medical Centre. The data set includes radiographs of upper limb fractures involving the radius, ulna, and humerus in...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | All Life |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/26895293.2022.2064923 |
| _version_ | 1797236953317376000 |
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| author | Chia Fong Lau Sorayya Malek Roshan Gunalan WH Chee A Saw Firdaus Aziz |
| author_facet | Chia Fong Lau Sorayya Malek Roshan Gunalan WH Chee A Saw Firdaus Aziz |
| author_sort | Chia Fong Lau |
| collection | DOAJ |
| description | To analyse and predict the healing time of upper limb fractures in children, machine learning algorithms were used. Paediatric orthopaedic data was obtained from the University Malaya Medical Centre. The data set includes radiographs of upper limb fractures involving the radius, ulna, and humerus in children under the age of twelve, with ages recorded from the date and time of initial injury. Inputs assessment included: age, gender, bone type, the number of bones involved, fracture type, angulation and the distance of the fracture. Random Forest (RF) and Support Vector Regression (SVR) algorithms were used to predict and identify variables associated with fracture healing time. Self Organizing Maps was then used for visualization and ordination of factors associated with healing time. Algorithms performance was measured using root mean square error (RMSE). A significant determinant in fracture healing includes age, bone part, fracture angulation, and distance. The Wilcoxon signed ranked test reported there is a significant difference between the prediction result of the SVR model (RMSE = 2.56) and the RF model (RMSE = 2.66). Predicting healing time can be used in the treatment process and follow up period for general practitioners and medical officers. The algorithm is deployed online at https://kidsfractureexpert.com/. |
| first_indexed | 2024-03-08T16:59:15Z |
| format | Article |
| id | doaj.art-cb794e229a864d01b475feadb83b3f5c |
| institution | Directory Open Access Journal |
| issn | 2689-5307 |
| language | English |
| last_indexed | 2024-04-24T17:12:02Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | All Life |
| spelling | doaj.art-cb794e229a864d01b475feadb83b3f5c2024-03-28T09:48:50ZengTaylor & Francis GroupAll Life2689-53072022-12-0115149049910.1080/26895293.2022.20649232064923Paediatric upper limb fracture healing time prediction using a machine learning approachChia Fong Lau0Sorayya Malek1Roshan Gunalan2WH Chee3A Saw4Firdaus Aziz5University of MalayaUniversity of MalayaUniversity of MalayaUniversity of MalayaUniversity of MalayaUniversity of MalayaTo analyse and predict the healing time of upper limb fractures in children, machine learning algorithms were used. Paediatric orthopaedic data was obtained from the University Malaya Medical Centre. The data set includes radiographs of upper limb fractures involving the radius, ulna, and humerus in children under the age of twelve, with ages recorded from the date and time of initial injury. Inputs assessment included: age, gender, bone type, the number of bones involved, fracture type, angulation and the distance of the fracture. Random Forest (RF) and Support Vector Regression (SVR) algorithms were used to predict and identify variables associated with fracture healing time. Self Organizing Maps was then used for visualization and ordination of factors associated with healing time. Algorithms performance was measured using root mean square error (RMSE). A significant determinant in fracture healing includes age, bone part, fracture angulation, and distance. The Wilcoxon signed ranked test reported there is a significant difference between the prediction result of the SVR model (RMSE = 2.56) and the RF model (RMSE = 2.66). Predicting healing time can be used in the treatment process and follow up period for general practitioners and medical officers. The algorithm is deployed online at https://kidsfractureexpert.com/.http://dx.doi.org/10.1080/26895293.2022.2064923upper limbpaediatric orthopaedicsupport vector regressionrandom forestself-organising mapsmachine learning |
| spellingShingle | Chia Fong Lau Sorayya Malek Roshan Gunalan WH Chee A Saw Firdaus Aziz Paediatric upper limb fracture healing time prediction using a machine learning approach All Life upper limb paediatric orthopaedic support vector regression random forest self-organising maps machine learning |
| title | Paediatric upper limb fracture healing time prediction using a machine learning approach |
| title_full | Paediatric upper limb fracture healing time prediction using a machine learning approach |
| title_fullStr | Paediatric upper limb fracture healing time prediction using a machine learning approach |
| title_full_unstemmed | Paediatric upper limb fracture healing time prediction using a machine learning approach |
| title_short | Paediatric upper limb fracture healing time prediction using a machine learning approach |
| title_sort | paediatric upper limb fracture healing time prediction using a machine learning approach |
| topic | upper limb paediatric orthopaedic support vector regression random forest self-organising maps machine learning |
| url | http://dx.doi.org/10.1080/26895293.2022.2064923 |
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