Exploratory analysis using machine learning of predictive factors for falls in type 2 diabetes

We aimed to investigate the status of falls and to identify important risk factors for falls in persons with type 2 diabetes (T2D) including the non-elderly. Participants were 316 persons with T2D who were assessed for medical history, laboratory data and physical capabilities during hospitalization...

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Published in:Scientific reports 2022-07, Vol.12 (1), p.11965-11965, Article 11965
Main Authors: Suzuki, Yasuhiro, Suzuki, Hiroaki, Ishikawa, Tatsuya, Yamada, Yasunori, Yatoh, Shigeru, Sugano, Yoko, Iwasaki, Hitoshi, Sekiya, Motohiro, Yahagi, Naoya, Hada, Yasushi, Shimano, Hitoshi
Format: Article
Language:English
Subjects:
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Diabetes
Diabetes mellitus (non-insulin dependent)
Diabetic retinopathy
Falls
Humanities and Social Sciences
Knee
Mathematical models
multidisciplinary
Regression analysis
Retinopathy
Risk factors
Science
Science (multidisciplinary)
Statistical analysis
Statistical models
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Summary:We aimed to investigate the status of falls and to identify important risk factors for falls in persons with type 2 diabetes (T2D) including the non-elderly. Participants were 316 persons with T2D who were assessed for medical history, laboratory data and physical capabilities during hospitalization and given a questionnaire on falls one year after discharge. Two different statistical models, logistic regression and random forest classifier, were used to identify the important predictors of falls. The response rate to the survey was 72%; of the 226 respondents, there were 129 males and 97 females (median age 62 years). The fall rate during the first year after discharge was 19%. Logistic regression revealed that knee extension strength, fasting C-peptide (F-CPR) level and dorsiflexion strength were independent predictors of falls. The random forest classifier placed grip strength, F-CPR, knee extension strength, dorsiflexion strength and proliferative diabetic retinopathy among the 5 most important variables for falls. Lower extremity muscle weakness, elevated F-CPR levels and reduced grip strength were shown to be important risk factors for falls in T2D. Analysis by random forest can identify new risk factors for falls in addition to logistic regression.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-15224-4