Comparison of Machine Learning Models to Predict Risk of Falling in Osteoporosis Elderly

Abstract

<jats:title>Abstract</jats:title><jats:p>Falls are a multifactorial cause of injuries for older people. Subjects with osteoporosis are more vulnerable to falls. The focus of this study is to investigate the performance of the different machine learning models built on spatiotemporal gait parameters to predict falls particularly in subjects with osteoporosis. Spatiotemporal gait parameters and prospective registration of falls were obtained from a sample of 110 community dwelling older women with osteoporosis (age 74.3 ± 6.3) and 143 without osteoporosis (age 68.7 ± 6.8). We built four different models, Support Vector Machines, Neuronal Networks, Decision Trees, and Dynamic Bayesian Networks (DBN), for each specific set of parameters used, and compared them considering their accuracy, precision, recall and F-score to predict fall risk. The F-score value shows that DBN based models are more efficient to predict fall risk, and the best result obtained is when we use a DBN model using the experts’ variables with FSMC’s variables, mixed variables set, obtaining an accuracy of 80%, and recall of 73%. The results confirm the feasibility of computational methods to complement experts’ knowledge to predict risk of falling within a period of time as high as 12 months.</jats:p>

Machine Learning Solutions for Osteoporosis—A Review

“…Predicting the risk of bone loss, osteoporotic fractures, falls, or comorbidities in osteoporotic patients over time was investigated in 14 studies (Table 4). ( 98–111 ) Two of them used unsupervised learning to identify fracture and comorbidity risk groups, respectively. ( 98,99 ) Kruse and colleagues developed a fracture risk clustering model to categorize subgroups of patients at risk.…”

“…( 98 ) Wang and colleagues investigated osteoporotic patients' subgroups and their related comorbidity risk. ( 99 ) The 12 remaining studies used supervised learning for the prediction of risk of osteoporosis by bone density loss at 10 years, ( 100 ) incident falls at 6 months ( 102 ) and 1 year, ( 101 ) incident vertebral fracture at ≈ 8 months, ( 103 ) hip fracture prediction at 4, 5, or 10 years, ( 107–111 ) vertebral or hip fractures at ≈ 7.5 years, ( 104 ) major osteoporotic fractures (hip, spine, wrist, or humerus) at ≈ 4.5 years, ( 105 ) and all sort of fracture sites at 1 and 2 years. ( 106 ) Because unsupervised learning is not intended to predict a predetermined outcome, no performance metrics were reported.…”

“…Based on this checklist, the most critical methodological and reporting limitations observed were presence of overfitting risk, ( 29,33,35,37,40,48,51,54,58–60,63,65 ) lack of model selection configuration reporting, ( 23–25,29,33,35,39,41,42,45,46,50,51,53,56–61,64,65 ) reporting of almost perfect model performances, ( 29,43,48,51,54,56,60,72,92 ) or lack of confidence intervals around point estimates. ( 33,34,44,52–65,72,73,86,88,92,102 ) In general, the studies where these issues were observed were not adequately acknowledged. ( 29,51,60,72,92 ) Acknowledging potential biases or limitations is fundamental for the accurate interpretation and especially for claims related to clinical implication.…”

Applications of Machine Learning in Bone and Mineral Research

“…With regard to predicting outcomes other than fracture, few studies have attempted to predict bone loss and falls [60][61][62].…”

“…With regard to predicting outcomes other than fracture, few studies have attempted to predict bone loss and falls [ 60 – 62 ]. The rate of bone loss over 10 years could be predicted better with the artificial neural network than with multiple regression analysis using conventional parameters, such as age, body mass index, menopause, fat and lean body mass, and BMD values [ 60 ].…”