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Annals of Bariatric Surgery، جلد ۸، شماره ۲، صفحات ۹-۱۳
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| عنوان انگلیسی |
A machine learning approach to predict types of bariatric surgery using the patients first physical exam information |
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| چکیده انگلیسی مقاله |
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. |
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| کلیدواژه‌های انگلیسی مقاله |
Bariatric surgery, Machine learning, Roux-en-Y Gastric Bypass, Sleeve Gastrostomy, Mini-gastric Bypass/One-Anastomosis Gastric Bypass |
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| نویسندگان مقاله |
| Ali Sheidaei
Minimally Invasive Surgery Research Center, Iran University of Medical Sciences,Tehran,Iran
| Seyed Amin Setaredan
Minimally Invasive Surgery Research Center, Iran University of Medical Sciences,Tehran,Iran
| Fatemeh Soleimany
Department of Biostatistics, Faculty of Public Health, Iran University of Medical Sciences, Tehran, Iran
| Kimiya Gohari
Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| Amirhossein Aliakbar
Department of Biostatistics, Faculty of Public Health, Iran University of Medical Sciences, Tehran, Iran
| Negar Zamaninour
Minimally Invasive Surgery Research Center, Iran University of Medical Sciences,Tehran,Iran
| Abdolreza Pazouki
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
| Ali Kabir
Minimally Invasive Surgery Research Center, Iran University of Medical Sciences,Tehran,Iran
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| نشانی اینترنتی |
http://annbsurg.iums.ac.ir/browse.php?a_code=A-10-29-1&slc_lang=en&sid=1 |
| فایل مقاله |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
Metabolic Surgery |
| نوع مقاله منتشر شده |
Original |
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