Prediction of Scoliosis Risk in Adolescents with Machine Learning Models

Home Page
About
Submit A Journal
Submit A Conference
Submit Paper/Book
- Submit a Preprint
- Submit a Book
Contact

Artificial Intelligence Theory and Applications
Volume:4 Issue:1
Prediction of Scoliosis Risk in Adolescents with Machine Learning Models

Prediction of Scoliosis Risk in Adolescents with Machine Learning Models

Authors : Murat Ali Çınar, İbrahim Küçükcan

Pages : 33-42

View : 105 | Download : 60

Publication Date : 2024-05-01

Article Type : Research Paper

Abstract :When examining classifications related to scoliosis, \"Idiopathic Scoliosis\" emerges as the most prevalent type. Alongside spinal alterations, patients with scoliosis experience changes in stability and gait while standing. Although there are existing studies in the literature regarding the progression of scoliosis and its impact on plantar pressure among individuals diagnosed with adolescent idiopathic scoliosis, no studies have been found on predicting scoliosis risk in healthy adolescents. This study aims to develop a decision support system based on artificial neural networks (ANN) capable of predicting scoliosis risk in adolescents using foot pressure analysis values and machine learning models. The study included 20 patients diagnosed with Adolescent Idiopathic Scoliosis and 43 healthy adolescent individuals with similar demographic characteristics (totaling 63 patients). Plantar pressure distributions of all participants were measured statically and dynamically. Data collected for all patients included: age, gender, right hindfoot static plantar pressure percentage, left hindfoot static plantar pressure percentage, right forefoot static plantar pressure percentage, left forefoot static plantar pressure percentage, right foot dynamic plantar pressure percentage, and left foot dynamic plantar pressure percentage. A dataset was compiled with pressure percentages and the presence of scoliosis diagnosis information (comprising 8 input variables and 1 result variable for each patient). The top performers in predicting adolescent idiopathic scoliosis risk were determined to be: Subspace KNN (100%), RUS Boosted Trees (100%), Weighted KNN (100%), Bagged Trees (100%), and Fine KNN (100%).
Keywords : Adolescent idiopathic scoliosis, plantar pressure distribution, machine learning

ORIGINAL ARTICLE URL

* There may have been changes in the journal, article,conference, book, preprint etc. informations. Therefore, it would be appropriate to follow the information on the official page of the source. The information here is shared for informational purposes. IAD is not responsible for incorrect or missing information.