Machine Learning Aplicado a Triagem de Osteoporose: modelo baseado na atenuação de ondas eletromagnéticas
Osteoporosis is a silent and still underdiagnosed condition, with a mortality rate higher than several types of cancer, especially when patients suffer fractures. The gold standard equipment for the diagnosis, Dual-energy X-ray absorptiometry (DXA, or DEXA), which uses ionizing radiation and is e...
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| Formato: | doctoralThesis |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/55342 |
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| Resumo: | Osteoporosis is a silent and still underdiagnosed condition, with a mortality rate higher than
several types of cancer, especially when patients suffer fractures. The gold standard
equipment for the diagnosis, Dual-energy X-ray absorptiometry (DXA, or DEXA), which uses
ionizing radiation and is expensive, is scarce in countries considered middle or low-income,
thus hindering timely access to diagnosis. In this context, a portable device, Osseus, was
developed for the screening of patients who need the densitometry exam, i.e., to qualify the
referrals of exams to the DEXA equipment. The thesis aimed to validate the Osseus device
using machine learning techniques. For this, the planning and data collection of 505 patients
who underwent the exam at DEXA and Osseus. 21.8% of them were healthy and 78.2% were
diseased (they had low bone mineral density or osteoporosis). The dataset was separated into
80% for training and validation (5-fold cross-validation) and 20% for testing. The performance
obtained in the test base with the best model (Random Forest) corresponded to
sensitivity=0.853, specificity=0.871, and F1(harmonic average of precision and sensitivity
rate)=0.859. The results showed that the most relevant variables to indicate the individual
health status were age, body mass index (BMI), and the attenuation of the signal emitted and
detected by the Osseus device.When compared to the results of DEXA scans, the model has
proven to be effective and consistent in screening individuals with osteoporosis and facilitating
early diagnosis of the disease, which consequently entails improved productivity and reduced
costs for surgery, treatment, and hospitalization. Thus, by qualifying the referral of patients
from primary care to the specialized network, Osseus can impact the reduction of waiting lines
of the Brazilian National Health System. |
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