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

Among 8 machine learning algorithms, the model showing the best performance was gradient boosting, for which the mean (SE) MSE value was 4.219 (0.140) and the mean (SE) R2 value was 0.967 (0.001). The hyperparameters for the gradient boosting model were α=0.9, complexity parameter α=0, learning rate=0.1, maximum depth=5, and number of trees=500. The second-best performing algorithm was the SVM model, with a mean (SE) MSE of 8.244 (0.210) and mean (SE) R2 value of 0.935 (0.002).

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

JMIR Aging 2025;8:e64473

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

Model 2 included 11 variables that were used in the classification system developed by Park et al [9], including age, sex, emergency operation, operation duration, diabetes, ACEi or ARB usage, blood levels of albumin, hemoglobin, sodium, e GFR, and urine dipstick protein. In this model, light GBM (AUC=0.81) and DNN (AUC=0.8) showed the highest performance.

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