Abstract
Falls are a multifactorial cause of injuries for older people. Subjects with osteoporosis are particularly vulnerable to falls. We study the performance of different computational methods to identify people with osteoporosis who experience a fall by analysing balance parameters. Balance parameters, from eyes open and closed posturographic studies, and prospective registration of falls were obtained from a sample of 126 community-dwelling older women with osteoporosis (age 74.3 ± 6.3) using World Health Organization Questionnaire for the study of falls during a follow-up of 2.5 years. We analyzed model performance to determine falls of every developed model and to validate the relevance of the selected parameter sets. The principal findings of this research were (1) models built using oversampling methods with either IBk (KNN) or Random Forest classifier can be considered good options for a predictive clinical test and (2) feature selection for minority class (FSMC) method selected previously unnoticed balance parameters, which implies that intelligent computing methods can extract useful information with attributes which otherwise are disregarded by experts. Finally, the results obtained suggest that Random Forest classifier using the oversampling method to balance the data independent of the set of variables used got the best overall performance in measures of sensitivity (>0.71), specificity (>0.18), positive predictive value (PPV >0.74), and negative predictive value (NPV >0.66) independent of the set of variables used. Although the IBk classifier was built with oversampling data considering information from both eyes opened and closed, using all variables got the best performance (sensitivity >0.81, specificity >0.19, PPV = 0.97, and NPV = 0.66).
Highlights
Falls are a major threat to the quality of life of older adults. e risk of falling is multifactorial but can be decreased if predisposing factors are addressed [1]. erefore, the identification of predisposing factors is essential
It is important to mention that the results reported in the works cited are a global value, i.e., the authors do not report the score obtained about the faller classification
Diagnosis of osteoporosis was made based on their DXA results according to World Health Organization (WHO) definitions (T score lower than 2.5 standard deviations of the mean peak bone mass for healthy adults at one or more skeletal sites)
Summary
Falls are a major threat to the quality of life of older adults. e risk of falling is multifactorial but can be decreased if predisposing factors are addressed [1]. erefore, the identification of predisposing factors is essential. E risk of falling is multifactorial but can be decreased if predisposing factors are addressed [1]. Clinical guidelines recommend screening the risk of falling in older adults at least once a year [1, 2]. Balance, [3] aging, [4], and osteoporosis [5]. Several methods have been developed to assess balance problems and the risk of falling in the elderly population with positive results [4, 6]. The use of clinical scales may be insufficient to predict falls in special population such as people suffering from osteoporosis [5]. It has been reported that women with osteoporosis present balance particularities which compromise their stability and predispose them to fall [7].
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