Paediatric upper limb fracture healing time prediction using a machine learning approach. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Lau, Chia Fong; Malek, Sorayya; Gunalan, Roshan; Chee, WH; Saw, A; Aziz, Firdaus
• Is Part Of: All life. Volume 15:Issue 1(2022)
• Page Start: 490
• Page End: 499
• Publication Date: 2022-12-31
• Subjects: Upper limb -- paediatric orthopaedic -- Support Vector Regression -- Random Forest -- self-organising maps -- machine learning; Life sciences -- Periodicals; Biology -- Periodicals; Electronic journals; Periodicals; 570.5
• DOI: 10.1080/26895293.2022.2064923
• Languages: English
• ISSNs: 2689-5293

Abstract

Abstract : 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/ .