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Volume 8, Issue 2 (Annals of Bariatric Surgery 2019)
ABS 2019, 8(2): 9-13 |
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Sheidaei A, Setaredan S A, Soleimany F, Gohari K, Aliakbar A, Zamaninour N, et al . A machine learning approach to predict types of bariatric surgery using the patients first physical exam information. ABS 2019; 8 (2) :9-13
URL: http://annbsurg.iums.ac.ir/article-1-240-en.html
URL: http://annbsurg.iums.ac.ir/article-1-240-en.html
Ali Sheidaei1 
, Seyed Amin Setaredan1 , Fatemeh Soleimany2 , Kimiya Gohari3 , Amirhossein Aliakbar2 , Negar Zamaninour1 , Abdolreza Pazouki4 , Ali Kabir 1
, Seyed Amin Setaredan1 , Fatemeh Soleimany2 , Kimiya Gohari3 , Amirhossein Aliakbar2 , Negar Zamaninour1 , Abdolreza Pazouki4 , Ali Kabir 1
1- Minimally Invasive Surgery Research Center, Iran University of Medical Sciences,Tehran,Iran
2- Department of Biostatistics, Faculty of Public Health, Iran University of Medical Sciences, Tehran, Iran
3- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
4- Minimally Invasive Surgery Research Center, Iran University of Medical Sciences,Tehran,Iran. Center of Excellence for Minimally Invasive Surgery Education, Iran University of Medical Sciences
2- Department of Biostatistics, Faculty of Public Health, Iran University of Medical Sciences, Tehran, Iran
3- Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
4- Minimally Invasive Surgery Research Center, Iran University of Medical Sciences,Tehran,Iran. Center of Excellence for Minimally Invasive Surgery Education, Iran University of Medical Sciences
Abstract: (881 Views)
Background: According to the IFSO worldwide survey report in 2014, 579517 bariatric operations have been performed in a year, of which nearly half the procedures were SG followed by RYGB. This procedure is a proven successful treatment of patients with morbid obesity which induces considerable weight loss and improvement of type 2 diabetes mellitus, insulin resistance, inflammation, and vascular function. In the present study, we aimed to build a machine based on a decision tree to mimics the surgeons pathway to select the type of bariatric surgery for patients.
Material and methods: We used patient’s data from the National Bariatric Surgery registry between March 2009 and October 2020. A decision tree was constructed to predict the type of surgery. The validation of the decision tree confirmed using 4-folds cross-validation.
Results: We rich a decision tree with a depth of 5 that is able to classify 77% of patients into correct surgery groups. In addition, using this model we are able to predict 99% of bypass cases (Sensitivity) correctly. The waist circumference less than 126 cm and BMI equal to or more than 43 kg/m2, age equal to or greater than 30 years old, and being hypertensive or diabetes are the most important separators.
Discussion: The effects of all nodes have been studied before and the references confirmed the relations of them and surgery type.
Material and methods: We used patient’s data from the National Bariatric Surgery registry between March 2009 and October 2020. A decision tree was constructed to predict the type of surgery. The validation of the decision tree confirmed using 4-folds cross-validation.
Results: We rich a decision tree with a depth of 5 that is able to classify 77% of patients into correct surgery groups. In addition, using this model we are able to predict 99% of bypass cases (Sensitivity) correctly. The waist circumference less than 126 cm and BMI equal to or more than 43 kg/m2, age equal to or greater than 30 years old, and being hypertensive or diabetes are the most important separators.
Discussion: The effects of all nodes have been studied before and the references confirmed the relations of them and surgery type.
Keywords: Bariatric surgery, Machine learning, Roux-en-Y Gastric Bypass, Sleeve Gastrostomy, Mini-gastric Bypass/One-Anastomosis Gastric Bypass
Type of Study: Original |
Subject:
Metabolic Surgery
Received: 2020/12/22 | Accepted: 2020/12/24 | ePublished: 2020/12/20
Received: 2020/12/22 | Accepted: 2020/12/24 | ePublished: 2020/12/20
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