Automatic Estimation of Osteoporotic Fracture Cases by Using Ensemble Learning Approaches

Abstract

Ensemble learning methods are one of the most powerful tools for the pattern classification problems. In this paper, the effects of ensemble learning methods and some physical bone densitometry parameters on osteoporotic fracture detection were investigated. Six feature set models were constructed including different physical parameters and they fed into the ensemble classifiers as input features. As ensemble learning techniques, bagging, gradient boosting and random subspace (RSM) were used. Instance based learning (IBk) and random forest (RF) classifiers applied to six feature set models. The patients were classified into three groups such as osteoporosis, osteopenia and control (healthy), using ensemble classifiers. Total classification accuracy and f-measure were also used to evaluate diagnostic performance of the proposed ensemble classification system. The classification accuracy has reached to 98.85 % by the combination of model 6 (five BMD + five T-score values) using RSM-RF classifier. The findings of this paper suggest that the patients will be able to be warned before a bone fracture occurred, by just examining some physical parameters that can easily be measured without invasive operations.

Machine Learning Solutions for Osteoporosis—A Review

“…( 42,52 ) As outcome, studies classified osteoporotic versus normal patients, ( 29,36,39,40,43,49,50,52,54–57,62 ) osteoporotic versus non‐osteoporotic patients (based on a BMD T ‐score threshold of –2.5 SD), ( 34,38,44,64 ) normal versus abnormal subjects (based on the BMD T ‐score threshold of −1 SD), ( 33,41,42,45,47,48,58–60,65 ) experimented multiple classifications, ( 46,63 ) or assigned to three classes: osteoporosis (BMD T ‐score ≤ −2.5 SD), osteopenia (−2.5 < BMD T ‐score ≤ −1), and normal (BMD T ‐score > −1 SD). ( 32,37,51,61 ) The models were internally validated in almost each study, and in two of them the models were also externally validated. ( 32,50 ) Twelve studies validated their model using accuracy with an average performance of 90.1% (range 70.0% to 98.9%); 22 validated their model using AUC with a mean of 0.90 (range 0.74 to 1.00).…”

“…( 29,32–34,36–65 ) Osteoporosis classification was made based on lumbar BMD, ( 32–34,37,51 ) hip BMD, ( 38,50,58 ) lumbar and hip BMD, ( 29,39–42,46–48,53,59,60 ) other non‐standard assessments, ( 43,44,49,54–56,65 ) or unspecified. ( 36,45,52,57,61–64 ) Studies identified osteoporosis based on opportunistic imaging from CT, ( 32–34 ) X‐ray, ( 37,38,43–45,55–59,63,64 ) or dental imaging; (36,47–49,53,54,60,62 ) other studies used data from patient characteristics, ( 40,41,50,51,61,65 ) bone biomarkers, (29,39 ) or acoustical responses. ( 42,52 ) As outcome, studies classified osteoporotic versus normal patients, ( 29,36,39,40,43,49,50,52,54–57,62 ) osteoporotic versus non‐osteoporotic patients (based on a BMD T ‐score threshold of –2.5 SD), ( 34,38,44,64 ) normal versus abnormal subjects (based on the BMD T ‐score threshold of −1 SD), ( 33,41,42,45,47,48,58–60,65 ) experimented multiple classifications, ( 46,63 ) or assigned to three classes: osteoporosis (BMD T ‐score ≤ −2.5 SD), osteopenia (−2.5 < BMD T ‐score ≤ −1), and normal (BMD T ‐score > −1 SD).…”

Clasificación de la densidad mineral ósea utilizando técnicas de aprendizaje automático en niños y adolescentes según edad y sexo

“…Los algoritmos que se van a evaluar son los siguientes: Forest PA [23], J48 [24], Random Forest [14], EBMC [25], TAN, NB Tree [25], DTNB [26], FURIA [27] y Classification VIa Regression, que son algoritmos populares en el campo de la clasificación de datos de salud.…”

“…Las técnicas de clasificadores toman los datos de cada paciente y predicen la presencia de enfermedades basadas en patrones subyacentes. Las máquinas de vectores de soporte (MVS) [13], los random forest (RF) [14] y las redes neuronales artificiales (RNA) [15] han sido enfoques ampliamente utilizados en el aprendizaje automático [2].…”

A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis

“…Osteoporotic vertebral fracture is occurred due to bone-osteoporosis in which vertebral bones are shirked as the bone mass is lowered. In vertebral osteoporosis, bones become narrow and weaken with osteoporosis that causes the fracture [8]. Statistics reveal that approximately eight point nine million fractures of vertebral column are caused due to osteoporosis on annual basis, which means that osteoporotic vertebral fracture is caused after every three seconds worldwide [9].…”