Predictive study of diabetic readmission with coupled A-RES and integrated learning algorithm

Yiming Zou; Yicheng Gongt; Xiaojie Wang

doi:10.1117/12.2629156

22 April 2022 Predictive study of diabetic readmission with coupled A-RES and integrated learning algorithm

Yiming Zou, Yicheng Gongt, Xiaojie Wang

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 1217412 (2022) https://doi.org/10.1117/12.2629156
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

Repeated hospitalizations have brought a heavy economic burden to the society. Exploring the establishment of a dynamic prediction model for readmission is helpful for early intervention. Based on the machine learning algorithm, a dynamic prediction model of diabetes readmission combined with the A-RES algorithm is proposed. SMOTE oversampling for data imbalance use, encoding processing and One-Hot encoding processing for medical texts are used to train prediction models of random forest, GBDT, and XGBOOST, and the A-RES algorithm is used to combine them for training. The learning results show that the GBDT+RES model has the best performance, with an AUC score of 0.973 points, which is an increase of 0.12 points compared with the original model. The final prediction models all have good model performance.

Citation Download Citation

Yiming Zou, Yicheng Gongt, and Xiaojie Wang "Predictive study of diabetic readmission with coupled A-RES and integrated learning algorithm", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 1217412 (22 April 2022); https://doi.org/10.1117/12.2629156

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