【論文】XGBoostモデルを用いて高齢者の転倒リスクを予測する

Result:

The model accuracy in classifying fall risk levels ranged between 67–70% with 43–53% sensitivity and 77–84% specificity. Noh, B., Youm, C., Goh, E., Lee, M., Park, H., Jeon, H., & Kim, O. Y. (2021). XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes. Scientific reports, 11(1), 12183.

https://doi.org/10.1038/s41598-021-91797-w

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