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Journals / Online Türk Sağlık Bilimleri Dergisi / 2026 / Cilt: 9 Sayı: 1

Determining the Landing Error Scoring System after a Jump by Artificial Intelligence

Sıçramadan Sonra Yere İniş Hata Puanlama Sistemi’nin Yapay Zeka İle Belirlenmesi

Sabriye Ercan(Suleyman Demirel University, Faculty of Medicine)0000-0001-9500-698X
Ahmet Ali Süzen(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ)0000-0002-5871-1652
Ferdi Başkurt(SÜLEYMAN DEMİREL ÜNİVERSİTESİ)0000-0002-8997-4172
Zeliha Başkurt(SÜLEYMAN DEMİREL ÜNİVERSİTESİ)0000-0001-7488-9242
Pages
14–20
Publication Date
2024-03-11
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AI Summary & Key Takeaways

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

Conclusions: The score given to the LESS's 8th, 10th, 16th, and 17th items can be predicted with high accuracy, and the total score can be reached through the model proposed in the research.

📌 3 Key Takeaways

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Objective & Hypothesis:Objective: The study aims to examine the predictability of the Landing Error Scoring System (LESS) results after the jump with the Adaptive Boosting (AdaBoost) algorithm.
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Key Findings & Data:Materials and Methods: A model has been developed by artificial intelligence to shorten the scoring system significantly.
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Conclusion & Impact:Conclusions: The score given to the LESS's 8th, 10th, 16th, and 17th items can be predicted with high accuracy, and the total score can be reached through the model proposed in the research.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:AdaBoost modeli · sıçrama · sıçramadan sonra yere iniş hata puanlama sistemi

Abstract

Objective: The study aims to examine the predictability of the Landing Error Scoring System (LESS) results after the jump with the Adaptive Boosting (AdaBoost) algorithm. Materials and Methods: A model has been developed by artificial intelligence to shorten the scoring system significantly. In the data preprocessing stage, 17 different items contained in the original dataset were reduced to 13. A total of 3790 data items were included in the dataset used in the study, and the dataset was divided into 4 different sub-datasets. AdaBoost was chosen to give the highest accuracy tested in five different machine learning used for regression. The model's reliability was evaluated by testing the proposed AdaBoost model with performance metrics. Results: The error score given by the clinician in the LESS was in the range of 0-86.6%. Recommended AdaBoost model for Sub1, Sub2, Sub3, and Sub4 respectively 98%, 87%, 88%, 89% accuracy has been achieved. Conclusions: The score given to the LESS's 8th, 10th, 16th, and 17th items can be predicted with high accuracy, and the total score can be reached through the model proposed in the research.

Özet

Amaç: Çalışmada, Adaptive Boosting (AdaBoost) algoritması ile Sıçramadan Sonra Yere İniş Hata Puanlama Sistemi (SSYİ-HPS) sonuçlarının öngörülebilirliğinin incelenmesi amaçlanmıştır. Materyal ve Metot: Puanlama sistemini daha da kısaltmak için yapay zeka yardımıyla bir model geliştirilmiştir. Veri ön işleme aşamasında, orijinal veri setinde yer alan 17 farklı madde 13'e düşürülmüştür. Çalışmada kullanılan veri setinde toplam 3790 veri yer almış ve veri seti 4 farklı alt veri setine ayrılmıştır. Regresyon için kullanılan beş farklı makine öğrenim modelinden en yüksek doğruluğu veren AdaBoost seçilmiştir. Modelin başarısı, önerilen AdaBoost modelinin performans metrikleri ile test edilmesiyle değerlendirilmiştir. Bulgular: SSYİ-HPS'de klinisyen tarafından verilen hata puanı %0-86,6 aralığındaydı. Önerilen AdaBoost modelinde sırasıyla Sub1, Sub2, Sub3 ve Sub4 için %98, %87, %88, %89 doğruluk sağlanmıştır. Sonuç: Araştırmada önerilen model ile SSYİ-HPS’nin 8., 10., 16. ve 17. maddelerine verilen puan yüksek doğrulukla tahmin edilebilmekte ve toplam puana ulaşılabilmektedir.

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Cite This Article

Ercan, S., Süzen, A. A., Başkurt, F., Başkurt, Z. (2024). Determining the Landing Error Scoring System after a Jump by Artificial Intelligence. Online Türk Sağlık Bilimleri Dergisi, Cilt: 9 Sayı: 1, 14–20. https://doi.org/10.26453/otjhs.1296565

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