Detection of Onset and Progression of Osteoporosis Using Machine Learning

Kerketta, Shilpi Ruchi; Ghosh, Debalina

doi:10.1002/9781119792611.ch9

Cited by 5 publications

(1 citation statement)

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“…The metrics obtained for the RF model were similar to those found by Kerketta 20 in which the Random Forest classifier was considered the best when compared to the decision tree and KNNeighbor (KNN) and was able to clearly identify the different stages of loss of bone mass density in the presence of tissue variations by computer simulation 20 . Our results reinforce the previous results of this algorithm's performance in the prediction of osteoporosis.…”

Section: Discussionsupporting

confidence: 73%

Osteoporosis screening using machine learning and electromagnetic waves

Albuquerque

Carvalho

Cruz

et al. 2023

Sci Rep

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Osteoporosis is a disease characterized by impairment of bone microarchitecture that causes high socioeconomic impacts in the world because of fractures and hospitalizations. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing the disease, access to DXA in developing countries is still limited due to its high cost, being present only in specialized hospitals. In this paper, we analyze the performance of Osseus, a low-cost portable device based on electromagnetic waves that measures the attenuation of the signal that crosses the medial phalanx of a patient’s middle finger and was developed for osteoporosis screening. The analysis is carried out by predicting changes in bone mineral density using Osseus measurements and additional common risk factors used as input features to a set of supervised classification models, while the results from DXA are taken as target (real) values during the training of the machine learning algorithms. The dataset consisted of 505 patients who underwent osteoporosis screening with both devices (DXA and Osseus), of whom 21.8% were healthy and 78.2% had low bone mineral density or osteoporosis. A cross-validation with k-fold = 5 was considered in model training, while 20% of the whole dataset was used for testing. The obtained performance of the best model (Random Forest) presented a sensitivity of 0.853, a specificity of 0.879, and an F1 of 0.859. Since the Random Forest (RF) algorithm allows some interpretability of its results (through the impurity check), we were able to identify the most important variables in the classification of osteoporosis. The results showed that the most important variables were age, body mass index, and the signal attenuation provided by Osseus. The RF model, when used together with Osseus measurements, is effective in screening patients and facilitates the early diagnosis of osteoporosis. The main advantages of such early screening are the reduction of costs associated with exams, surgeries, treatments, and hospitalizations, as well as improved quality of life for patients.

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Section: Discussionsupporting

confidence: 73%