Fuzzy classification of bariatric post-surgery effectiveness

Aldo Arévalo; Ricardo Pacheco; Cátia M. Salgado; Saskia van Loon; Arjen Kars Boer; Susana M. Vieira; Uzay Kaymak; João M.C. Sousa

doi:10.1109/FUZZ-IEEE.2018.8491643

Fuzzy classification of bariatric post-surgery effectiveness

Aldo Arévalo, Ricardo Pacheco, Cátia M. Salgado, Saskia van Loon, Arjen Kars Boer, Susana M. Vieira, Uzay Kaymak, João M.C. Sousa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

Abstract

The expected post-operatory weight loss is not always achieved after bariatric surgery. Efforts have been done to describe the causes. Recently, total weight loss (%TWL) has been pointed out to better assess weight loss in bariatric patients. However, there is no cut off point that delimits the patients who successfully achieve their weight goals after a bariatric surgery. In this work, a method based on fuzzy modeling is implemented to help clinicians setting up the best cut-off point in %TWL for a specific population. The best boundary to delimit success and failure will be selected based on the predictive performance of the assessed cut-off points: 25, 30, 35 and 40%TWL after one and two years of surgery. Area under the receiver operating characteristic curve (AUC) values of 0.70 and 0.75 were achieved for the first and second post-surgery periods, respectively. Further, features not previously described as predictors of weight loss were identified as good predictors of the outcome.

Original language	English
Title of host publication	2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	8
ISBN (Electronic)	978-1-5090-6020-7
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2018.8491643
Publication status	Published - 12 Oct 2018
Event	2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018

Conference

Conference	2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018
Abbreviated title	FUZZ 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

Funding

This work was supported by FCT, through IDMEC, under project (UID/EMS/50022/2013). Susana M. Vieira acknowledges the support by Program Investigador FCT (IF/00833/2014) from FCT, co-funded by the European Social Fund (ESF) through the Operational Program Human Potential (POPH). The work of Cátia M. Salgado was supported by the PhD Scholarship SFRH/BD/121875/2016 from FCT. The work of Aldo Arévalo was supported by the PhD Scholarship PD/BD/114107/2015 from FCT. The authors acknowledge the Bariatric center of the Catharina Hospital for granting the access to the database.

Keywords

Bariatrics
Fuzzy modeling
Obesity
Total weight loss

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/FUZZ-IEEE.2018.8491643

Cite this

Arévalo, A., Pacheco, R., Salgado, C. M., van Loon, S., Boer, A. K., Vieira, S. M., Kaymak, U., & Sousa, J. M. C. (2018). Fuzzy classification of bariatric post-surgery effectiveness. In 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings Article 8491643 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/FUZZ-IEEE.2018.8491643

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title = "Fuzzy classification of bariatric post-surgery effectiveness",

abstract = "The expected post-operatory weight loss is not always achieved after bariatric surgery. Efforts have been done to describe the causes. Recently, total weight loss (%TWL) has been pointed out to better assess weight loss in bariatric patients. However, there is no cut off point that delimits the patients who successfully achieve their weight goals after a bariatric surgery. In this work, a method based on fuzzy modeling is implemented to help clinicians setting up the best cut-off point in %TWL for a specific population. The best boundary to delimit success and failure will be selected based on the predictive performance of the assessed cut-off points: 25, 30, 35 and 40%TWL after one and two years of surgery. Area under the receiver operating characteristic curve (AUC) values of 0.70 and 0.75 were achieved for the first and second post-surgery periods, respectively. Further, features not previously described as predictors of weight loss were identified as good predictors of the outcome.",

keywords = "Bariatrics, Fuzzy modeling, Obesity, Total weight loss",

author = "Aldo Ar{\'e}valo and Ricardo Pacheco and Salgado, {C{\'a}tia M.} and {van Loon}, Saskia and Boer, {Arjen Kars} and Vieira, {Susana M.} and Uzay Kaymak and Sousa, {Jo{\~a}o M.C.}",

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Arévalo, A, Pacheco, R, Salgado, CM, van Loon, S, Boer, AK, Vieira, SM, Kaymak, U & Sousa, JMC 2018, Fuzzy classification of bariatric post-surgery effectiveness. in 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings., 8491643, Institute of Electrical and Electronics Engineers, Piscataway, 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018, Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/FUZZ-IEEE.2018.8491643

Fuzzy classification of bariatric post-surgery effectiveness. / Arévalo, Aldo; Pacheco, Ricardo; Salgado, Cátia M. et al.
2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings. Piscataway: Institute of Electrical and Electronics Engineers, 2018. 8491643.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Salgado, Cátia M.

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AU - Boer, Arjen Kars

AU - Vieira, Susana M.

AU - Kaymak, Uzay

AU - Sousa, João M.C.

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N2 - The expected post-operatory weight loss is not always achieved after bariatric surgery. Efforts have been done to describe the causes. Recently, total weight loss (%TWL) has been pointed out to better assess weight loss in bariatric patients. However, there is no cut off point that delimits the patients who successfully achieve their weight goals after a bariatric surgery. In this work, a method based on fuzzy modeling is implemented to help clinicians setting up the best cut-off point in %TWL for a specific population. The best boundary to delimit success and failure will be selected based on the predictive performance of the assessed cut-off points: 25, 30, 35 and 40%TWL after one and two years of surgery. Area under the receiver operating characteristic curve (AUC) values of 0.70 and 0.75 were achieved for the first and second post-surgery periods, respectively. Further, features not previously described as predictors of weight loss were identified as good predictors of the outcome.

AB - The expected post-operatory weight loss is not always achieved after bariatric surgery. Efforts have been done to describe the causes. Recently, total weight loss (%TWL) has been pointed out to better assess weight loss in bariatric patients. However, there is no cut off point that delimits the patients who successfully achieve their weight goals after a bariatric surgery. In this work, a method based on fuzzy modeling is implemented to help clinicians setting up the best cut-off point in %TWL for a specific population. The best boundary to delimit success and failure will be selected based on the predictive performance of the assessed cut-off points: 25, 30, 35 and 40%TWL after one and two years of surgery. Area under the receiver operating characteristic curve (AUC) values of 0.70 and 0.75 were achieved for the first and second post-surgery periods, respectively. Further, features not previously described as predictors of weight loss were identified as good predictors of the outcome.

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ER -

Fuzzy classification of bariatric post-surgery effectiveness

Abstract

Conference

Funding

Keywords

UN SDGs

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