Open AccessArtificial neural network in prediction of pelvic organ prolapse
Author(s) -
Alexey V. Galkin,
Галкин Алексей Викторович,
Natalya G. Galkina,
Галкина Наталья Геннадиевна,
Oleg I. Kaganov,
Каганов Олег Игоревич,
N. S. Karamysheva,
Карамышева Надежда Сергеевна,
Е.А. Калинина,
Калинина Екатерина Александровна,
Igor S. Shapovalov,
Шаповалов Игорь Сергеевич
Publication year - 2020
Publication title -
aspirantskij vestnik povolžʹâ
Language(s) - English
Resource type - Journals
eISSN - 2410-3764
pISSN - 2072-2354
DOI - 10.17816/2072-2354.2020.20.3.132-137
Subject(s) - medicine , urinary incontinence , childbirth , urinary system , surgery , pregnancy , genetics , biology
The aim of this study was to assess the possibility of using an artificial neural network in predicting pelvic organ prolapse. 180 patients were selected from the urological database, of which 62 had pelvic organ prolapse, in 118 cases prolapse was not detected. Data analysis was carried out with the use of the artificial neural network (ANN). As a result, the most important risk factors or predictors for the development of pelvic organ prolapse include the number of births, the number of pregnancies, chronic obstructive pulmonary disease, prolapse of the heart valves, as well as accessory chords, urinary incontinence before/after childbirth, BMI. Artificial neuron network can potentially be useful in decision-making on the development of preventive measures aimed at the prophylaxis of pelvic organ prolapse.