Identification of Risk Factors for Scoliosis in Elementary School Children Using Machine Learning

Authors

  • Ahmad Aizat Che Rahmat Ministry of Health Malaysia, Pahang
  • Siti Zura A. Jalil University Teknologi Malaysia, Kuala Lumpur
  • Sharifah Alwiah Syed Abd Rahman University Teknologi Malaysia, Kuala Lumpur
  • Sahnius Usman University Teknologi Malaysia, Kuala Lumpur
  • Mohammad Shabbir Alam College of Computer and Information Technology, Jazan University

Keywords:

Backpack weight, Angle of trunk rotation, Scoliosis, Elementary school, Decision Tree, KNN

Abstract

Scoliosis is an abnormal curvature of the spine and often diagnosed in childhood or early adolescence. In this study, the risk factors for scoliosis in elementary school children is investigate based on age, backpack weight and gender. There are 260 children participated in this study from aged 7 up to 12 years old. Scoliometer is used to measure the angle of trunk rotation (ATR) on Adam Forward Bending Test. Statistical analysis of analysis of variance (ANOVA) is used to determine the characteristic difference of ATR readings on the risk factors for scoliosis. Significant results with P-value less than 0.001 are found among ATR readings on a linear combination of risk factors for scoliosis of age and backpack weight. Then, the risk factors for scoliosis are classified among elementary school children using Decision Tree and K-Nearest Neighbor. The classification results shown that both Decision Tree method produced highest classification percentage up to 98.08%. This finding indicates that age and backpack weight are significant as the risk factors for scoliosis.

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Published

31-07-2023

How to Cite

Che Rahmat, A. A. ., A. Jalil, S. Z. ., Syed Abd Rahman, S. A. ., Usman, S. ., & Alam, M. S. . (2023). Identification of Risk Factors for Scoliosis in Elementary School Children Using Machine Learning. International Journal of Integrated Engineering, 15(3), 94–103. Retrieved from https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/12823

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