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Journals / Artificial Intelligence Theory and Applications / 2026 / Volume: 4 Issue: 1

Prediction of Scoliosis Risk in Adolescents with Machine Learning Models

Murat Ali Çınar(HASAN KALYONCU ÜNİVERSİTESİ)0000-0003-2122-3759
İbrahim Küçükcan(ÜTOPYA FİZYOTERAPİ ve DANIŞMANLIK MERKEZİ)0000-0001-5962-289X
Pages
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Publication Date
2024-05-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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%).

📌 3 Key Takeaways

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Objective & Hypothesis:When examining classifications related to scoliosis, "Idiopathic Scoliosis" emerges as the most prevalent type.
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Key Findings & Data:The study included 20 patients diagnosed with Adolescent Idiopathic Scoliosis and 43 healthy adolescent individuals with similar demographic characteristics (totaling 63 patients).
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Conclusion & Impact: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%).
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Adolescent idiopathic scoliosis · plantar pressure distribution · machine learning

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%).

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Cite This Article

Çınar, M. A., Küçükcan, İ. (2024). Prediction of Scoliosis Risk in Adolescents with Machine Learning Models. Artificial Intelligence Theory and Applications, Volume: 4 Issue: 1. https://ajindex.com/articles/1609609

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