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A Comparative Study of Machine Learning Algorithms in Predicting Severe Complications after Bariatric Surgery
Örebro University, School of Medical Sciences. Örebro University Hospital. (Clinical Epidemiology and Biostatistics)ORCID iD: 0000-0002-3552-9153
Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Örebro University, School of Medical Sciences. Örebro University Hospital. Department of Surgery.
Division of Surgery, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.ORCID iD: 0000-0002-0166-6344
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2019 (English)In: Journal of Clinical Medicine, ISSN 2077-0383, Vol. 8, no 5, article id 668Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 8, no 5, article id 668
Keywords [en]
machine learning, bariatric surgery, severe complication, prediction, comparative study
National Category
Surgery General Practice
Identifiers
URN: urn:nbn:se:oru:diva-75270DOI: 10.3390/jcm8050668ISI: 000470992500103PubMedID: 31083643OAI: oai:DiVA.org:oru-75270DiVA, id: diva2:1338840
Funder
Stockholm County CouncilNovo Nordisk
Note

Funding Agencies:

Örebro Region County Council  

Örebro University 

SRP Diabetes

Available from: 2019-07-24 Created: 2019-07-24 Last updated: 2019-07-24Bibliographically approved

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Cao, YangOttosson, JohanStenberg, Erik

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