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Table 3 Fall risk classification models

From: Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review

Author Model Acc (%) Sen (%) Spe (%)
Bautmans et al. [40] logistic regression analysis, ROC 77.0 78.0 78.0
Bizovska et al. [43] logistic regression analysis, ROC 53.0 85.0
Buisseret et al. [64] a binary classification, ROC 85.7 50.0 73,9
Greene et al. [55] ROC 79.7 73.1 82.6
Gietzelt et al. [36] decision tree 75.0 78.2 71.2
Howcroft et al. [56] support vector machine and neural networks 80.0–84.0 50.0–66.7 89.5
Hua et al. [41] random forests 73.7 81.1
Ihlen et al. [44] Partial Least Square Regression Analysis 76.0 (SF)
68.0 (MF)
71.0 (SF)
67.0 (MF)
80.0 (SF)
69.0 (MF)
Ihlen et al. [49] Partial Least Square Discriminatory Analysis 59.0–88.0 77.0–92.0
Iluz et al. [35] Ada Boost, Support Vector Machine, Bag, Naïve Bayes 87.1–90.6 83.8–89.2 87.2–94.4
Marschollek et al. [62] logistic regression, classification model 70.0 58.0 78.0
Marschollek et al. [61] a classification trees 90.0 57.7 100.0
Qui et al. [50] a logistic regression, Naïve Bayes, decision tree, boosted tree, random forest, support vector machine 79.7–89.4 87.2–92.7 69.2–84.9
Rivolta et al. [19] a linear model, artificial neural network 71.0–86.0 81.0–90.0
Sample et al. [58] a stepwise logistic regression, max-rescaled R2 value 48.1 82.1
Senden et al. [51] linear regression analysis, ROC 76.0 70.0
van Schooten et al. [52] logistic regression analysis, ROC 67.9 66.3
Weiss et al. [54] a binary logistic regression analysis 71.6 62.1 78.9
Weiss et al. [53] binary logistic regression analysis 87.8 91.3 83.3
  1. a These models also include data of clinical assessment (e. g. body mass index)
  2. Acc: accuracy, Sen: sensitivity, Spe: specificity, ROC: receiver operating curve, SF: single faller, MF: multiple faller