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Journals / Politeknik Dergisi / 2026 / Cilt: 28 Sayı: 5

Artificial Intelligence in Healthcare: Fall Risk Assessment in Older Adults by Using Machine Learning Techniques

Sağlık Hizmetinde Yapay Zeka: Makine Öğrenmesi Teknikleri Kullanılarak Yaşlılarda Düşme Riski Tespiti

Gökçe Özden Gürcan(ESKISEHIR OSMANGAZI UNIVERSITY, ESKİŞEHİR VOCATIONAL SCHOOL)0000-0002-5386-1985
Hakan Gokdas(ESKISEHIR TECHNICAL UNIVERSİTY, FACULTY OF ENGINEERING)0000-0003-3399-8658
Ebru Turan Kızıldoğan(ESKISEHIR OSMANGAZI UNIVERSITY, ESKİŞEHİR HEALTH SERVICES VOCATIONAL SCHOOL)0000-0002-2005-604X
Pages
1415–1423
Publication Date
2025-10-12
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AI Summary & Key Takeaways

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

Based on the results of fall prediction, individualized fall prevention interventions can be developed to reduce the fall rates of elderly individuals.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:There are many attempts to provide the elderly with a life more independently.
📊
Key Findings & Data:Falls are one of the most common accidents among the elderly and may result in extended hospitalization and increased medical costs.
💡
Conclusion & Impact:Based on the results of fall prediction, individualized fall prevention interventions can be developed to reduce the fall rates of elderly individuals.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Düşme Riski · Makine Öğrenimi · Lojistik Regresyon

Abstract

There are many attempts to provide the elderly with a life more independently. One of the main problems facing people in this age group is fall events. Falls are one of the most common accidents among the elderly and may result in extended hospitalization and increased medical costs. The requirement for care services, such as fall detection, is increasing because of the growing population of elderly people. In this study, machine learning techniques- Logistic Regression, Random Forest, and Decision Tree are used to predict fall risk of elderly people. Fall risk assessment methods are used to obtain inputs and outputs in addition to the physical and clinical features of people in the dataset. This study aimed to facilitate the fall risk assessment process of health professionals to determine the fall risk factors of elderly individuals and to make predictions. Based on the results of fall prediction, individualized fall prevention interventions can be developed to reduce the fall rates of elderly individuals.

Özet

Yaşlılara daha bağımsız bir yaşam sağlamak için birçok girişimde bulunulmaktadır. Bu yaş grubundaki insanların karşılaştığı temel sorunlardan biri düşme olaylarıdır. Düşme, yaşlılar arasında en sık görülen kazalardan biridir ve hastanede kalış süresinin uzamasına ve tıbbi maliyetlerin artmasına neden olabilmektedir. Yaşlı nüfusun artması nedeniyle düşme tespiti gibi bakım hizmetlerine olan ihtiyaç da artmaktadır. Bu çalışmada yaşlıların düşme riskinin tahmin edilmesi amacıyla makine öğrenmesi teknikleri (Logistic Regresyon, Random Forest, Decision Tree) kullanılmıştır. Veri setinde yer alan kişilerin fiziksel ve klinik özelliklerinin yanı sıra girdi ve çıktıların elde edilmesi için düşme riski değerlendirme yöntemleri kullanılmıştır. Bu çalışma, yaşlı bireylerin düşme risk faktörlerini belirlemek ve tahminlerde bulunmak amacıyla, sağlık profesyonellerinin düşme riski değerlendirme sürecini kolaylaştırmak amacıyla yapılmıştır. Düşme tahmininin sonuçlarına dayanarak bu sayede, yaşlı bireylerin düşme oranlarını azaltmak için bireyselleştirilmiş düşme önleme müdahaleleri geliştirilebilecektir.

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Cite This Article

Gürcan, G. Ö., Gokdas, H., Kızıldoğan, E. T. (2025). Artificial Intelligence in Healthcare: Fall Risk Assessment in Older Adults by Using Machine Learning Techniques. Politeknik Dergisi, Cilt: 28 Sayı: 5, 1415–1423. https://doi.org/10.2339/politeknik.1446723

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