Journal of Medical Internet Research

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Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis

Doreen Phiri, Frank Makowa, Vivi Leona Amelia, Yohane Vincent Abero Phiri, Lindelwa Portia Dlamini, Min-Huey Chung

J Med Internet Res 2025;27:e59002

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Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study

Chang-Uk Jeong, Jacob S Leiby, Dokyoon Kim, Eun Kyung Choe

JMIR Aging 2025;8:e64473

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Assessment of Gait Parameters Using Wearable Sensors and Their Association With Muscle Mass, Strength, and Physical Performance in Korean Older Adults: Cross-Sectional Study

Jinyoung Shin, Hyuk Jung Kweon, Jaekyung Choi

JMIR Form Res 2025;9:e63928

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A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation

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.

Ji Won Min, Jae-Hong Min, Se-Hyun Chang, Byung Ha Chung, Eun Sil Koh, Young Soo Kim, Hyung Wook Kim, Tae Hyun Ban, Seok Joon Shin, In Young Choi, Hye Eun Yoon