TY  - JOUR
PY  - 2021//
TI  - Simplified decision-tree algorithm to predict falls for community-dwelling older adults
JO  - Journal of clinical medicine
A1  - Makino, Keitaro
A1  - Lee, Sangyoon
A1  - Bae, Seongryu
A1  - Chiba, Ippei
A1  - Harada, Kenji
A1  - Katayama, Osamu
A1  - Tomida, Kouki
A1  - Morikawa, Masanori
A1  - Shimada, Hiroyuki
SP  - e5184
EP  - e5184
VL  - 10
IS  - 21
N2  - The present study developed a simplified decision-tree algorithm for fall prediction with easily measurable predictors using data from a longitudinal cohort study: 2520 community-dwelling older adults aged 65 years or older participated. Fall history, age, sex, fear of falling, prescribed medication, knee osteoarthritis, lower limb pain, gait speed, and timed up and go test were assessed in the baseline survey as fall predictors. Moreover, recent falls were assessed in the follow-up survey. We created a fall-prediction algorithm using decision-tree analysis (C5.0) that included 14 nodes with six predictors, and the model could stratify the probabilities of fall incidence ranging from 30.4% to 71.9%. Additionally, the decision-tree model outperformed a logistic regression model with respect to the area under the curve (0.70 vs. 0.64), accuracy (0.65 vs. 0.62), sensitivity (0.62 vs. 0.50), positive predictive value (0.66 vs. 0.65), and negative predictive value (0.64 vs. 0.59). Our decision-tree model consists of common and easily measurable fall predictors, and its white-box algorithm can explain the reasons for risk stratification; therefore, it can be implemented in clinical practices. Our findings provide useful information for the early screening of fall risk and the promotion of timely strategies for fall prevention in community and clinical settings.<p />  <p>Language: en</p>
LA  - en
SN  - 2077-0383
UR  - http://dx.doi.org/10.3390/jcm10215184
ID  - ref1
ER  -