Abstract
Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.
Highlights
The continuous increase in life expectancy raises the incidence of problems related to the weakening of the body due to age
The mean values, standard deviations, and best result among the 1,000 runs are shown in the tables describing the performance of the different models; all results correspond with the test sets
The eight attributes selected as the result of applying Principal Component Analysis (PCA) and correlation were taken into account; besides, an analysis with 12 features adding the four features mentioned in Section 2.2.3 was considered
Summary
The continuous increase in life expectancy raises the incidence of problems related to the weakening of the body due to age. Among the diseases and medical conditions that afflict the countries of the first world, next to the cardiovascular and nervous system ones, but very underestimated in comparison, there are the problems related to bones. According to data from the International Osteoporosis Foundation (IOF), approximately 1.6 million hip fractures occur around the world each year, and in 2050, this number will increase to figures between 4.5 and 6.3 million, due mainly to the aging population [2]. In the case of Spain, in 2015, this disease was suffered by 2.2 million women and 0.6 million men, which is practically 1% of the current Spanish population. According to the IOF, it is estimated that around 330,000 fragility fractures occurred in this country in 2017
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