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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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For instance, Zhao et al [23] constructed machine learning models to predict the p CR to NAC based on clinicopathological variables. Similarly, Zhang and coworkers [24-30] developed machine learning models that incorporated clinicopathological features, radiomic features, and pathomic features to forecast the p CR following NAC. Additionally, Sammut et al [31] and Chen et al [32] created models using multi-omics data.
J Med Internet Res 2025;27:e69864
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Citation Accuracy Challenges Posed by Large Language Models
JMIR Med Educ 2025;11:e72998
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