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Development of a 5-Year Risk Prediction Model for Transition From Prediabetes to Diabetes Using Machine Learning: Retrospective Cohort Study

Development of a 5-Year Risk Prediction Model for Transition From Prediabetes to Diabetes Using Machine Learning: Retrospective Cohort Study

Cat Boost: categorical boosting; Light GBM: light gradient boosting machine; MLP: multiplayer perceptron; SVM: support vector machine; XGBoost: extreme gradient boosting machine. The SHAP analysis of the best-performing Cat Boost model is shown in Figure 4.

Yongsheng Zhang, Hongyu Zhang, Dawei Wang, Na Li, Haoyue Lv, Guang Zhang

J Med Internet Res 2025;27:e73190

Machine-Aided Self-diagnostic Prediction Models for Polycystic Ovary Syndrome: Observational Study

Machine-Aided Self-diagnostic Prediction Models for Polycystic Ovary Syndrome: Observational Study

The goal of this proposed study is to develop a machine-aided self-diagnostic tool that predicts the diagnosis of PCOS with and without any invasive measures, using Principal Component Analysis (PCA), k-means clustering algorithm, and Cat Boost classifier. The Cat Boost method is one of the newer gradient boosting decision tree models, and it was recently used in diabetes prediction in the study by Kumar et al [20].

Angela Zigarelli, Ziyang Jia, Hyunsun Lee

JMIR Form Res 2022;6(3):e29967