Bariatric surgery (BAR) has become a popular treatment for type 2 diabetes mellitus which is among the most critical obesity-related comorbidities. Patients who have bariatric surgery, are exposed to complications after surgery. Furthermore, the mid- to long-term complications after bariatric surgery can be deadly and increase the complexity of managing safety of these operations and healthcare costs. Current studies on BAR complications have mainly used risk scoring for identifying patients who are more likely to have complications after surgery. Though, these studies do not take into consideration the imbalanced nature of the data where the size of the class of interest (patients who have complications after surgery) is relatively small. We propose the use of imbalanced classification techniques to tackle the imbalanced bariatric surgery data: synthetic minority oversampling technique (SMOTE), random undersampling, and ensemble learning classification methods including Random Forest, Bagging, and AdaBoost. Moreover, we improve classification performance through using Chi-squared, Information Gain, and Correlation-based feature selection techniques. We study the Premier Healthcare Database with focus on the most-frequent complications including Diabetes, Angina, Heart Failure, and Stroke. Our results show that the ensemble learning-based classification techniques using any feature selection method mentioned above are the best approach for handling the imbalanced nature of the bariatric surgical outcome data. In our evaluation, we find a slight preference toward using SMOTE method compared to the random undersampling method. These results demonstrate the potential of machine-learning tools as clinical decision support in identifying risks/outcomes associated with bariatric surgery and their effectiveness in reducing the surgery complications as well as improving patient care.
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Alexe, S., Blackstone, E., Hammer, P. L., Ishwaran, H., Lauer, M. S., & Snader, C. E. P. (2003). Coronary risk prediction by logical analysis of data. Annals of Operations Research, 119(1–4), 15–42.
Almdal, T., Scharling, H., Jensen, J. S., & Vestergaard, H. (2004). The independent effect of type 2 diabetes mellitus on ischemic heart disease, stroke, and death: A population-based study of 13,000 men and women with 20 years of follow-up. Archives of Internal Medicine, 164(13), 1422–1426.
American Diabetes Association. (2006). Diagnosis and classification of diabetes mellitus. Diabetes Care, 29(Supplement 1), S43–S48.
American Diabetes Association. (2015). Classification and diagnosis of diabetes. Diabetes Care, 38(Supplement 1), S8–S16.
Batista, G. E., Prati, R. C., & Monard, M. C. (2004). A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1), 20–29.
Blanco, R., Larrañaga, P., Inza, I., & Sierra, B. (2004). Gene selection for cancer classification using wrapper approaches. International Journal of Pattern Recognition and Artificial Intelligence, 18(08), 1373–1390.
Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Brolin, R. (1996). Gastrointestinal surgery for severe obesity. Nutrition, 12(6), 403–404.
Buchwald, H. (2005). Bariatric surgery for morbid obesity: Health implications for patients, health professionals, and third-party payers. Journal of the American College of Surgeons, 200(4), 593–604.
Buchwald, H., Avidor, Y., Braunwald, E., Jensen, M. D., Pories, W., Fahrbach, K., et al. (2004). Bariatric surgery: A systematic review and meta-analysis. JAMA, 292(14), 1724–1737.
Buchwald, H., Estok, R., Fahrbach, K., Banel, D., Jensen, M. D., Pories, W. J., et al. (2009). Weight and type 2 diabetes after bariatric surgery: Systematic review and meta-analysis. The American Journal of Medicine, 122(3), 248–256.
Cawley, J., & Meyerhoefer, C. (2012). The medical care costs of obesity: An instrumental variables approach. Journal of Health Economics, 31(1), 219–230.
Centers for Disease Control and Prevention. (2011). National diabetes fact sheet: National estimates and general information on diabetes and prediabetes in the United States, 2011. Atlanta, GA: US department of health and human services, centers for disease control and prevention, 201(1).
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
Daousi, C., Casson, I., Gill, G., MacFarlane, I., Wilding, J., & Pinkney, J. (2006). Prevalence of obesity in type 2 diabetes in secondary care: Association with cardiovascular risk factors. Postgraduate Medical Journal, 82(966), 280–284.
Deeba, F., Mohammed, S. K., Bui, F. M., & Wahid, K. A. (2016). An empirical study on the effect of imbalanced data on bleeding detection in endoscopic video. In 2016 IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC) (pp. 2598–2601). IEEE.
DeMaria, E. J., Portenier, D., & Wolfe, L. (2007). Obesity surgery mortality risk score: Proposal for a clinically useful score to predict mortality risk in patients undergoing gastric bypass. Surgery for Obesity and Related Diseases, 3(2), 134–140.
Fan, Y. J., & Chaovalitwongse, W. A. (2010). Optimizing feature selection to improve medical diagnosis. Annals of Operations Research, 174(1), 169–183.
Freund, Y., & Schapire, R. E. (1995). A desicion-theoretic generalization of on-line learning and an application to boosting. In European conference on computational learning theory (pp. 23–37). Springer.
Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning. Springer series in statistics (Vol. 1). Berlin: Springer.
Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F. (2012). A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 463–484.
Galar, M., Fernández, A., Barrenechea, E., & Herrera, F. (2013). EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recognition, 46(12), 3460–3471.
Grundy, S., Barondess, J., Bellegie, N., Fromm, H., Greenway, F., Halsted, C., et al. (1991). Gastrointestinal surgery for severe obesity. Annals of Internal Medicine, 115(12), 956–961.
Gu, Q., Zhu, L., & Cai, Z. (2009). Evaluation measures of the classification performance of imbalanced data sets. In International symposium on intelligence computation and applications (pp. 461–471). Springer.
Hall, M. A. (1999). Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato.
Hall, M. A. (2000). Correlation-based feature selection of discrete and numeric class machine learning. University of Waikato, Department of Computer Science.
Inza, I., Larrañaga, P., Blanco, R., & Cerrolaza, A. J. (2004). Filter versus wrapper gene selection approaches in DNA microarray domains. Artificial Intelligence in Medicine, 31(2), 91–103.
Inza, I., Larrañaga, P., Etxeberria, R., & Sierra, B. (2000). Feature subset selection by bayesian network-based optimization. Artificial Intelligence, 123(1–2), 157–184.
Jirapech-Umpai, T., & Aitken, S. (2005). Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes. BMC Bioinformatics, 6(1), 148.
Johnson, B. L., Blackhurst, D. W., Latham, B. B., Cull, D. L., Bour, E. S., Oliver, T. L., et al. (2013). Bariatric surgery is associated with a reduction in major macrovascular and microvascular complications in moderately to severely obese patients with type 2 diabetes mellitus. Journal of the American College of Surgeons, 216(4), 545–556.
Johnson, R. J., Johnson, B. L., Blackhurst, D. W., Bour, E. S., Cobb, W. S., Carbonell, A. M., et al. (2012). Bariatric surgery is associated with a reduced risk of mortality in morbidly obese patients with a history of major cardiovascular events. The American Surgeon, 78(6), 685–692.
Kannel, W. B., & McGee, D. L. (1979). Diabetes and cardiovascular disease: The Framingham study. JAMA, 241(19), 2035–2038.
Karegowda, A. G., Manjunath, A., & Jayaram, M. (2010). Comparative study of attribute selection using gain ratio and correlation based feature selection. International Journal of Information Technology and Knowledge Management, 2(2), 271–277.
Khalilia, M., Chakraborty, S., & Popescu, M. (2011). Predicting disease risks from highly imbalanced data using random forest. BMC Medical Informatics and Decision Making, 11(1), 51.
King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political Analysis, 9(2), 137–163.
Liaw, A., & Wiener, M. (2002). Classification and regression by randomforest. R News, 2(3), 18–22.
Li, L., Weinberg, C. R., Darden, T. A., & Pedersen, L. G. (2001). Gene selection for sample classification based on gene expression data: Study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics, 17(12), 1131–1142.
López, V., Fernández, A., García, S., Palade, V., & Herrera, F. (2013). An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Information Sciences, 250, 113–141.
Ogden, C. L., Carroll, M. D., Fryar, C. D., & Flegal, K. M. (2015). Prevalence of obesity among adults and youth: United States, 2011–2014. NCHS Data Brief, 219(219), 1–8.
Ooi, C., & Tan, P. (2003). Genetic algorithms applied to multi-class prediction for the analysis of gene expression data. Bioinformatics, 19(1), 37–44.
Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3), 21–45.
Pories, W. J. (2008). Bariatric surgery: Risks and rewards. The Journal of Clinical Endocrinology and Metabolism, 93(11 Supplement 1), s89–s96.
Quinlan, J. R. (2014). C4.5: Programs for machine learning. Amsterdam: Elsevier.
Razzaghi, T., Safro, I. (2015). Scalable multilevel support vector machines. In ICCS (pp. 2683–2687).
Razzaghi, T., Roderick, O., Safro, I., & Marko, N. (2016). Multilevel weighted support vector machine for classification on healthcare data with missing values. PLoS ONE, 11(5), e0155,119.
Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1), 1–39.
Roumani, Y. F., May, J. H., Strum, D. P., & Vargas, L. G. (2013). Classifying highly imbalanced ICU data. Health Care Management Science, 16(2), 119–128.
Roumani, Y. F., Roumani, Y., Nwankpa, J. K., & Tanniru, M. (2018). Classifying readmissions to a cardiac intensive care unit. Annals of Operations Research, 263(1–2), 429–451.
Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), 2507–2517.
Sarker, A., & Gonzalez, G. (2015). Portable automatic text classification for adverse drug reaction detection via multi-corpus training. Journal of Biomedical Informatics, 53, 196–207.
Schapire, R. E. (1990). The strength of weak learnability. Machine Learning, 5(2), 197–227.
Şeref, O., Razzaghi, T., & Xanthopoulos, P. (2017). Weighted relaxed support vector machines. Annals of Operations Research, 249(1–2), 235–271.
Stamler, J., Vaccaro, O., Neaton, J. D., & Wentworth, D. (1993). Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the multiple risk factor intervention trial. Diabetes Care, 16(2), 434–444.
Taft, L., Evans, R. S., Shyu, C., Egger, M., Chawla, N., Mitchell, J., et al. (2009). Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery. Journal of Biomedical Informatics, 42(2), 356–364.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: Practical machine learning tools and techniques. Burlington: Morgan Kaufmann.
World Health Organization. (2016). Global report on diabetes. World Health Organization.
Xiong, M., Fang, X., & Zhao, J. (2001). Biomarker identification by feature wrappers. Genome Research, 11(11), 1878–1887.
Yang, Y., & Pedersen, J. O. (1997). A comparative study on feature selection in text categorization. ICML, 97, 412–420.
Zadrozny, B., Langford, J., & Abe, N. (2003). Cost-sensitive learning by cost-proportionate example weighting. In Third IEEE international conference on data mining, 2003. ICDM 2003 (pp. 435–442). IEEE.
Zheng, B., Zhang, J., Yoon, S. W., Lam, S. S., Khasawneh, M., & Poranki, S. (2015). Predictive modeling of hospital readmissions using metaheuristics and data mining. Expert Systems with Applications, 42(20), 7110–7120.
This work was funded in part by Clemson University—Greenville Healthcare System postdoctoral program.
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Razzaghi, T., Safro, I., Ewing, J. et al. Predictive models for bariatric surgery risks with imbalanced medical datasets. Ann Oper Res 280, 1–18 (2019). https://doi.org/10.1007/s10479-019-03156-8
- Imbalanced data
- Risk prediction
- Clinical decision support
- Bariatric surgery