Predicting Fall Risk in Elderly Individuals: A Comparative Analysis of Machine Learning Models Using Patient Characteristics, Functional Balance Tests, and Computerized Dynamic Posturography

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Soylemez E., TOKGÖZ YILMAZ S.

Journal of Laryngology and Otology, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1017/s0022215124002160
  • Dergi Adı: Journal of Laryngology and Otology
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, CINAHL, MLA - Modern Language Association Database, Veterinary Science Database
  • Anahtar Kelimeler: Balance, Elderly Individuals, Fall Risk, Machine Learning, Posturography
  • Ankara Üniversitesi Adresli: Evet

Özet

Objective: This study aimed to predict the risk of falling using patient characteristics, computerized dynamic posturography, and functional balance tests (FBTs) in machine learning. Methods: One hundred twenty elderly individuals were included in this study. The fall status, physical characteristics, and medical history of individuals were investigated. Pure tone audiometry test, simple FBTs and sensory organization test (SOT) were applied to the individuals. Results: The machine learning model that incorporated comorbidities, physical characteristics, and functional balance tests achieved a 100% accuracy in predicting fall risk. Models using only comorbidities and physical characteristics, functional balance tests, or the SOT had accuracies of 87.5%, 83.34%, and 91.66%, respectively. Conclusion: Advanced balance systems are not always necessary to assess fall risk. Instead, fall risk can be effectively determined using simple balance tests, comorbidities, and patient characteristics in machine learning.