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Bone Disorders01:29

Bone Disorders

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Aging and its effect on bone remodeling is the most common cause of bone disorders. In young and healthy people, bone deposition and resorption happen at an equal rate to maintain optimal bone health.
Bone deposition is also affected by the levels of sex hormones like estrogen and testosterone that promote osteoblast activity and bone matrix synthesis. When the level of these hormones decreases due to aging, it causes a reduction in bone deposition. As a result, bone resorption by osteoclasts...
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Classification of Bones01:18

Classification of Bones

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
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Related Experiment Video

Updated: Jan 12, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Risk Prediction of Low Bone Density in Elderly Patients with Supervised Machine Learning Algorithms.

Eda Karaismailoğlu1, Serkan Karaismailoğlu2

  • 1Department of Medical Informatics, University of Health Sciences Türkiye, Gülhane Faculty of Medicine, Ankara, Türkiye.

Balkan Medical Journal
|October 31, 2025
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Summary
This summary is machine-generated.

Machine learning accurately predicts low bone mineral density (BMD) using patient data. Key risk factors identified include age, sex, and serum uric acid, aiding personalized osteoporosis screening.

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Area of Science:

  • Gerontology and Public Health
  • Biostatistics and Machine Learning
  • Medical Informatics

Background:

  • Low bone mineral density (BMD) is a significant age-related condition associated with increased fracture risk and mortality.
  • Machine learning (ML) offers a powerful tool for early prediction of low BMD by analyzing clinical, biochemical, and demographic data.

Purpose of the Study:

  • To assess the predictive capabilities of eleven ML models for identifying low BMD.
  • To pinpoint the most significant risk factors contributing to low BMD using the top-performing ML model.

Main Methods:

  • A cross-sectional study utilizing data from the National Health and Nutrition Examination Survey (2005-2020).
  • 12,108 participants aged ≥ 50 years with BMD data were analyzed using 57 features.
  • Supervised ML algorithms were trained and evaluated using metrics like accuracy and AUC, with SHAP analysis for predictor importance.

Main Results:

  • The Extra Trees classifier demonstrated superior performance with 76.7% accuracy and an AUC of 0.85.
  • Fourteen key predictors of low BMD were identified, including sex, age, BMI, race, serum uric acid, and diabetes status.
  • Established and novel risk factors were highlighted through feature importance analysis.

Conclusions:

  • Tree-based ML models, especially Extra Trees, are effective for predicting low BMD.
  • The study identified crucial risk factors, supporting personalized screening for osteoporosis and osteopenia.
  • ML effectively captures complex multifactorial relationships in population health data for BMD prediction.