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Forecast of Hospitalization Costs of Child Patients Based on Machine Learning Methods and Multiple Classification

Chenguang Wang 1, Xinyi Pan 1, Lishan Ye 2, Weifen Zhuang 3, and Fei Ma 1
1. Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, China
2. Information Department, Zhongshan Hospital of Xiamen University, Xiamen, China
3. School of Management, Xiamen University, Xiamen, China
Abstract—In the paper, Random Forest algorithm (RF), bagging and error-correction output code model (ECOC) were employed to predict the clinic expenditure of infantile patients with data consisting of records extracted from a hospital system. Throughout the modelling, the training set utilized 80% of the records selected from the original data set in random and the rest of data were used in the test set. The RF received superior predictive accuracy than bagging and ECOC, with RMSE being 0.138,
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