Diagnosis Osteoporosis Risk: Using Machine Learning Algorithms Among Fasa Adults Cohort Study (FACS).

Journal: Endocrinology, diabetes & metabolism
PMID: 39760233

Abstract

INTRODUCTION: In Iran, the assessment of osteoporosis through tools like dual-energy X-ray absorptiometry poses significant challenges due to their high costs and limited availability, particularly in small cities and rural areas. Our objective was to employ a variety of machine learning (ML) techniques to evaluate the accuracy and precision of each method, with the aim of identifying the most accurate pattern for diagnosing the osteoporosis risks.

Authors

  • Saghar Tabib
    Student Research Committee, School of Medicine, Fasa University of Medical Sciences, Fasa, Iran.
  • Seyed Danial Alizadeh
    Sina Trauma and Surgery Research Centre, Tehran University of Medical Sciences, Tehran, Iran.
  • Aref Andishgar
    USERN Office, Fasa University of Medical Sciences, Fasa, Iran.
  • Babak Pezeshki
    Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran.
  • Omid Keshavarzian
    School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
  • Reza Tabrizi
    School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

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