Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (8): 65-75    DOI: 10.11925/infotech.2096-3467.2021.0188
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Predicting Surgical Infections Based on Machine Learning
Su Qiang1,Hou Xiaoli1(),Zou Ni2
1School of Economics and Management, Tongji University, Shanghai 200092, China
2Shanghai General Hospital, Shanghai 200240, China
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Abstract

[Objective] This paper proposes a prediction model for post-operative infection based on a combined machine learning algorithm, aiming to effectively reduce surgical site infection risks. [Methods] First, we used SMOTE, ADASYN, and random oversampling to reduce the imbalance of the original data. Then, we combined five commonly used predictive models: Lasso, SVM, GBDT, ANN and RF to create a hybrid prediction method. Finally, we used the improved artificial bee colony algorithm to optimize the weight of multiple combinations. [Results] The G-mean and F1 values of the ABC combination strategy method reached 0.791 2 and 0.669 3 respectively, which were 15.15% and 23.62% higher than the existing ones. [Limitations] The sample size used in the study needs to be expanded. [Conclusions] The proposed model can effectively predict post-operative infections.

Received: 01 March 2021      Published: 15 September 2021
 ZTFLH: R619
Fund:National Natural Science Foundation of China(71972146);National Natural Science Foundation of China(71974127)
Corresponding Authors: Hou Xiaoli ORCID：0000-0003-3609-4734     E-mail: houxl@tongji.edu.cn