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Nonbinary data were one-hot encoded, a method for rearranging categorical data into binary variables, and numerical data were normalized using min-max scaling. This would convert all numeric values between or equal to a value of 0 and 1. Min-max scaling is given by:
One-hot encoding, min-max scaling, and dataset splitting were accomplished using the Scikit-Learn library (version 0.24.2) [24]. These steps are required to improve the performance of machine learning models and training stability.
J Med Internet Res 2025;27:e62853
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Survival curves were generated using the Kaplan-Meier (K-M) method, stratified by risk group identified using the RSF and the Cox regression model. Additionally, variable importance plots were created to illustrate the contribution of each variable to the models. Finally, the models were validated in the Duke and the SEER dataset.
J Med Internet Res 2025;27:e69864
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Once quantified, we can use k-means clustering to identify distinct clusters based on the gait patterns of patients [27]. The suitability of sample entropy for analyzing short and noisy biological datasets further enhances its applicability in this study [25]. Through comparing the sample entropy values, we classified patients into 2 clusters, namely the “active walking” and “inactive walking” groups, facilitating a more distinct interpretation of the obtained results.
J Med Internet Res 2025;27:e52694
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