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A Comparative Study of Machine Learning Algorithms in Predicting Severe Complications after Bariatric Surgery

Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, 70182 Örebro, Sweden
Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, 17177 Stockholm, Sweden
Department of Surgery, Faculty of Medicine and Health, Örebro University, 70182 Örebro, Sweden
Division of Surgery, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, 18288 Stockholm, Sweden
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
J. Clin. Med. 2019, 8(5), 668;
Received: 3 April 2019 / Revised: 8 May 2019 / Accepted: 10 May 2019 / Published: 12 May 2019
(This article belongs to the Special Issue The Future of Artificial Intelligence in Clinical Medicine)
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Background: Severe obesity is a global public health threat of growing proportions. Accurate models to predict severe postoperative complications could be of value in the preoperative assessment of potential candidates for bariatric surgery. So far, traditional statistical methods have failed to produce high accuracy. We aimed to find a useful machine learning (ML) algorithm to predict the risk for severe complication after bariatric surgery. Methods: We trained and compared 29 supervised ML algorithms using information from 37,811 patients that operated with a bariatric surgical procedure between 2010 and 2014 in Sweden. The algorithms were then tested on 6250 patients operated in 2015. We performed the synthetic minority oversampling technique tackling the issue that only 3% of patients experienced severe complications. Results: Most of the ML algorithms showed high accuracy (>90%) and specificity (>90%) in both the training and test data. However, none of the algorithms achieved an acceptable sensitivity in the test data. We also tried to tune the hyperparameters of the algorithms to maximize sensitivity, but did not yet identify one with a high enough sensitivity that can be used in clinical praxis in bariatric surgery. However, a minor, but perceptible, improvement in deep neural network (NN) ML was found. Conclusion: In predicting the severe postoperative complication among the bariatric surgery patients, ensemble algorithms outperform base algorithms. When compared to other ML algorithms, deep NN has the potential to improve the accuracy and it deserves further investigation. The oversampling technique should be considered in the context of imbalanced data where the number of the interested outcome is relatively small. View Full-Text
Keywords: machine learning; bariatric surgery; severe complication; prediction; comparative study machine learning; bariatric surgery; severe complication; prediction; comparative study

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Cao, Y.; Fang, X.; Ottosson, J.; Näslund, E.; Stenberg, E. A Comparative Study of Machine Learning Algorithms in Predicting Severe Complications after Bariatric Surgery. J. Clin. Med. 2019, 8, 668.

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