Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults

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...

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Autores principales:	Keitaro Makino, Sangyoon Lee, Seongryu Bae, Ippei Chiba, Kenji Harada, Osamu Katayama, Kouki Tomida, Masanori Morikawa, Hiroyuki Shimada
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fall prevention decision-tree machine learning risk prediction Medicine R
Acceso en línea:	https://doaj.org/article/80b9a318c69e45de9f7af0d5834d5ec3
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id	oai:doaj.org-article:80b9a318c69e45de9f7af0d5834d5ec3
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spelling	oai:doaj.org-article:80b9a318c69e45de9f7af0d5834d5ec32021-11-11T17:47:32ZSimplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults10.3390/jcm102151842077-0383https://doaj.org/article/80b9a318c69e45de9f7af0d5834d5ec32021-11-01T00:00:00Zhttps://www.mdpi.com/2077-0383/10/21/5184https://doaj.org/toc/2077-0383The 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.Keitaro MakinoSangyoon LeeSeongryu BaeIppei ChibaKenji HaradaOsamu KatayamaKouki TomidaMasanori MorikawaHiroyuki ShimadaMDPI AGarticlefall preventiondecision-treemachine learningrisk predictionMedicineRENJournal of Clinical Medicine, Vol 10, Iss 5184, p 5184 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fall prevention decision-tree machine learning risk prediction Medicine R
spellingShingle	fall prevention decision-tree machine learning risk prediction Medicine R Keitaro Makino Sangyoon Lee Seongryu Bae Ippei Chiba Kenji Harada Osamu Katayama Kouki Tomida Masanori Morikawa Hiroyuki Shimada Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults
description	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.
format	article
author	Keitaro Makino Sangyoon Lee Seongryu Bae Ippei Chiba Kenji Harada Osamu Katayama Kouki Tomida Masanori Morikawa Hiroyuki Shimada
author_facet	Keitaro Makino Sangyoon Lee Seongryu Bae Ippei Chiba Kenji Harada Osamu Katayama Kouki Tomida Masanori Morikawa Hiroyuki Shimada
author_sort	Keitaro Makino
title	Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults
title_short	Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults
title_full	Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults
title_fullStr	Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults
title_full_unstemmed	Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults
title_sort	simplified decision-tree algorithm to predict falls for community-dwelling older adults
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/80b9a318c69e45de9f7af0d5834d5ec3
work_keys_str_mv	AT keitaromakino simplifieddecisiontreealgorithmtopredictfallsforcommunitydwellingolderadults AT sangyoonlee simplifieddecisiontreealgorithmtopredictfallsforcommunitydwellingolderadults AT seongryubae simplifieddecisiontreealgorithmtopredictfallsforcommunitydwellingolderadults AT ippeichiba simplifieddecisiontreealgorithmtopredictfallsforcommunitydwellingolderadults AT kenjiharada simplifieddecisiontreealgorithmtopredictfallsforcommunitydwellingolderadults AT osamukatayama simplifieddecisiontreealgorithmtopredictfallsforcommunitydwellingolderadults AT koukitomida simplifieddecisiontreealgorithmtopredictfallsforcommunitydwellingolderadults AT masanorimorikawa simplifieddecisiontreealgorithmtopredictfallsforcommunitydwellingolderadults AT hiroyukishimada simplifieddecisiontreealgorithmtopredictfallsforcommunitydwellingolderadults
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Simplified Decision-Tree Algorithm to Predict Falls for Community-Dwelling Older Adults

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