[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.
苏强, 侯校理, 邹妮. 基于机器学习组合优化方法的术后感染预测模型研究*[J]. 数据分析与知识发现, 2021, 5(8): 65-75.
Su Qiang, Hou Xiaoli, Zou Ni. Predicting Surgical Infections Based on Machine Learning. Data Analysis and Knowledge Discovery, 2021, 5(8): 65-75.
Ke C Y, Jin Y, Evans H, et al. Prognostics of Surgical Site Infections Using Dynamic Health Data[J]. Journal of Biomedical Informatics, 2017, 65:22-33.
doi: 10.1016/j.jbi.2016.10.021
[2]
de Lissovoy G, Fraeman K, Hutchins V, et al. Surgical Site Infection: Incidence and Impact on Hospital Utilization and Treatment Costs[J]. American Journal of Infection Control, 2009, 37(5):387-397.
doi: S0196-6553(09)00073-X
pmid: 19398246
[3]
Hedrick T L, Sawyer R G, Friel C M, et al. A Method for Estimating the Risk of Surgical Site Infection in Patients with Abdominal Colorectal Procedures[J]. Diseases of the Colon & Rectum, 2013, 56(5):627-637.
[4]
Bilimoria K Y, Liu Y M, Paruch J L, et al. Development and Evaluation of the Universal ACS NSQIP Surgical Risk Calculator: A Decision Aid and Informed Consent Tool for Patients and Surgeons[J]. Journal of the American College of Surgeons, 2013, 217(5):833-842.
doi: 10.1016/j.jamcollsurg.2013.07.385
pmid: 24055383
[5]
Amri R, Dinaux A M, Kunitake H, et al. Risk Stratification for Surgical Site Infections in Colon Cancer[J]. JAMA Surgery, 2017, 152(7):686-690.
doi: 10.1001/jamasurg.2017.0505
[6]
Bergquist J R, Thiels C A, Etzioni D A, et al. Failure of Colorectal Surgical Site Infection Predictive Models Applied to an Independent Dataset: Do They Add Value or Just Confusion?[J]. Journal of the American College of Surgeons, 2016, 222(4):431-438.
doi: 10.1016/j.jamcollsurg.2015.12.034
pmid: 26847588
[7]
Bartz-Kurycki M A, Charles G, Anderson K T, et al. Enhanced Neonatal Surgical Site Infection Prediction Model Utilizing Statistically and Clinically Significant Variables in Combination with a Machine Learning Algorithm[J]. American Journal of Surgery, 2018, 216(4):764-777.
doi: S0002-9610(18)30093-X
pmid: 30078669
[8]
Grundmeier R W, Rui X, Ross R K, et al. Identifying Surgical Site Infections in Electronic Health Data Using Predictive Models[J]. Journal of the American Medical Informatics Association, 2018, 25(9):1160-1166.
doi: 10.1093/jamia/ocy075
pmid: 29982511
[9]
Kuo P J, Wu S C, Chien P C, et al. Artificial Neural Network Approach to Predict Surgical Site Infection after Free-Flap Reconstruction in Patients Receiving Surgery for Head and Neck Cancer[J]. Oncotarget, 2018, 9(17):13768-13782.
doi: 10.18632/oncotarget.v9i17
[10]
Zhu M, Xia J, Jin X Q, et al. Class Weights Random Forest Algorithm for Processing Class Imbalanced Medical Data[J]. IEEE Access, 2018, 6:4641-4652.
doi: 10.1109/ACCESS.2018.2789428
[11]
Guo X J, Yin Y L, Dong C L, et al. On the Class Imbalance Problem[C]// Proceedings of the 4th International Conference on Natural Computation. 2008: 192-201.
[12]
He H B, Garcia E A. Learning from Imbalanced Data[J]. IEEE Transactions on Knowledge & Data Engineering, 2009, 21(9):1263-1284.
[13]
Nekooeimehr I, Lai-Yuen S K. Adaptive Semi-unsupervised Weighted Oversampling (A-SUWO) for Imbalanced Datasets[J]. Expert Systems with Applications, 2015, 46:405-416.
doi: 10.1016/j.eswa.2015.10.031
[14]
Rivera W A, Xanthopoulos P. A Priori Synthetic Over-sampling Methods for Increasing Classification Sensitivity in Imbalanced Data Sets[J]. Expert Systems with Applications, 2016, 66:124-135.
doi: 10.1016/j.eswa.2016.09.010
[15]
Kourentzes N, Barrow D, Petropoulos F. Another Look at Forecast Selection and Combination: Evidence from Forecast Pooling[J]. International Journal of Production Economics, 2018, 209:226-235.
doi: 10.1016/j.ijpe.2018.05.019
( Li Jing, Liu Xiao, Wang Xiaoli. Financial Decision Knowledge Acquisition Based on Neighborhood Rough Set and Ensemble Classifiers with Grid Search[J]. Data Analysis and Knowledge Discovery, 2019, 3(1):85-94.)
( Shan Yinghao, Fu Qing, Geng Xuan, et al. Combined Forecasting of Photovoltaic Power Generation in Microgrid Based on the Improved BP-SVM-ELM and SOM-LSF with Particlization[J]. Proceedings of the CSEE, 2016, 36(12):3334-3343.)
[18]
Blanc S M, Setzer T. When to Choose the Simple Average in Forecast Combination[J]. Journal of Business Research, 2016, 69(10):3951-3962.
doi: 10.1016/j.jbusres.2016.05.013
( Liu Yang, Feng Yuqiang, Shao Zhen. Online Auction Final Price Forecasting Model Based on Bagging and Decision Tree[J]. Systems Engineering-Theory & Practice, 2009, 29(12):134-140.)
( Yang Guijun, Xu Xue, Zhao Fuqiang. Predicting User Ratings with XGBoost Algorithm[J]. Data Analysis and Knowledge Discovery, 2019, 3(1):118-126.)
[21]
Karaboga D, Basturk B. A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm[J]. Journal of Global Optimization, 2007, 39(3):459-471.
doi: 10.1007/s10898-007-9149-x
[22]
Horng M H. Multilevel Thresholding Selection Based on the Artificial Bee Colony Algorithm for Image Segmentation[J]. Expert Systems with Applications, 2011, 38(11):13785-13791.
[23]
Gao W F, Sheng H L, Wang J, et al. Artificial Bee Colony Algorithm Based on Novel Mechanism for Fuzzy Portfolio Selection[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(5):966-978.
doi: 10.1109/TFUZZ.91
[24]
Wang J, Wang Z, Li X, et al. Artificial Bee Colony-based Combination Approach to Forecasting Agricultural Commodity Prices[J/OL]. International Journal of Forecasting, 2019. https://doi.org/10.1016/j.ijforecast.2019.08.006.
[25]
Kiran M S, Hakli H, Gunduz M, et al. Artificial Bee Colony Algorithm with Variable Search Strategy for Continuous Optimization[J]. Information Sciences, 2015, 300:140-157.
doi: 10.1016/j.ins.2014.12.043
Alcalá-Fdez J, Fernández A, Luengo J, et al. KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework[J]. Journal of Multiple-Valued Logic and Soft Computing, 2011, 17:255-287.