یکشنبه 15 مهر 1403
جلد 8، شماره 2 - ( 9-1398 )
جلد 8 شماره 2 صفحات 13-9 |
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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-fa.html
URL: http://annbsurg.iums.ac.ir/article-1-240-fa.html
A machine learning approach to predict types of bariatric surgery using the patients first physical exam information. سالنامه جراحی چاقی. 1398; 8 (2) :9-13
چکیده: (993 مشاهده)
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.
نوع مطالعه: Original |
موضوع مقاله:
Metabolic Surgery
دریافت: 1399/10/2 | پذیرش: 1399/10/4 | انتشار الکترونیک: 1399/9/30
دریافت: 1399/10/2 | پذیرش: 1399/10/4 | انتشار الکترونیک: 1399/9/30
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