A feature-efficient dual-task machine learning framework for predicting bone mineral density and osteoporosis stratification in resource-constrained environments
Abstract
Osteoporosis is a chronic skeletal disorder characterized by progressive bone mineral density (BMD) loss and structural deterioration, significantly increasing fracture risk. Despite its high prevalence, early detection remains challenging due to its asymptomatic progression and the limitations of conventional diagnostic techniques, such as Dual-Energy X-ray Absorptiometry (DXA). While DXA remains the clinical benchmark for BMD assessment, its high cost, limited accessibility, and inability to directly detect vertebral fractures necessitate the development of alternative, cost-effective, and widely deployable diagnostic methodologies. A dataset of 159 patient records was collected from NORI and CDA Hospital, incorporating 17 input features spanning demographics, genetic/blood type, clinical history and lab tests parameters. To bridge this gap, we developed a practical machine learning model tailored for clinics with limited resources. Instead of relying on expensive imaging, our framework uses only basic, highly accessible clinical markers-specifically ABO blood groups, serum calcium, and potassium levels. Because these tests are inexpensive and easily processed in standard laboratories, our approach removes the financial and technical hurdles of advanced diagnostics, making early screening possible in remote or underfunded healthcare settings. Data preprocessing involved rigorous feature selection, standardization, hyper-parameters tuning, clinically relevant features…
Read the paper
Related studies
- Effectiveness of Bisphosphonates and Denosumab on Risk of Vertebral Fractures in Prostate Cancer Patients Under Androgen Deprivation Therapies: A Real-World Prospective Study
- Osteoporosis-related fractures after spinal cord injury: a retrospective study from Brazil
- Serum 25-hydroxyvitamin D, parathyroid hormone, and mortality in older men
- Lack of Association between Vitamin D Insufficiency and Cardiovascular or Fracture Risk: A UK Biobank Study
- Muscle strength and body composition are clinical indicators of osteoporosis
- Incidence of bone protection and associated fragility injuries in patients with proximal femur fractures
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.