[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120988-en":3,"doc-seo-120988-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120988,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques","Postpartum urinary incontinence is a widespread health issue among women after childbirth, with limited evidence on how pregnancy-related intrinsic and extrinsic variables contribute to later onset. This study evaluates the most influential variables using machine learning models built from intrinsic, extrinsic, and combined variable sets. Data from 93 pregnant patients were used to predict urinary incontinence, frequency, intensity, and stress urinary incontinence, applying oversampling and multiple classifiers. Results indicate extrinsic variables provide the most accurate predictions, suggesting prevention may be supported by healthy habits during pregnancy.","Original Research  \n| Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques\u003Cbr>José Alberto Benítez-Andrades1 , María Teresa García-Ordás2  , María Álvarez-González3, Raquel Leirós-Rodríguez4 and\u003Cbr>Ana F López Rodríguez3\u003Cbr>Abstract |  | Digital Health Volume 8: 1–13\u003Cbr>© The Author(s) 2022\u003Cbr>Article reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/20552076221111289](DOI: 10.1177/20552076221111289)[ ](DOI: 10.1177/20552076221111289)[journals.sagepub.com/home/dhj](journals.sagepub.com/home/dhj)\u003Cbr> |\n| --- | --- | --- |\n| Background: Postpartum urinary incontinence is a fairly widespread health problem in today’s society among women who have given birth. Recent studies analysing the different variables that may be related to Postpartum urinary incontinence have brought to light some variables that may be related to Postpartum urinary incontinence in order to try to prevent it. However, no studies have been found that analyse some of the intrinsic and extrinsic variables of patients during pregnancy that could give rise to this pathology.\u003Cbr>Objective: The objective of this study is to assess the most influential variables in Postpartum urinary incontinence by means of machine learning techniques, starting from a group of intrinsic variables, another group of extrinsic variables and a mixed group that combines both types.\u003Cbr>Methods: Information was collected on 93 patients, pregnant women who gave birth. Experiments were conducted using different machine learning classification techniques combined with oversampling techniques to predict four variables: urinary incontinence, urinary incontinence frequency, urinary incontinence intensity and stress urinary incontinence.\u003Cbr>Results: The results showed that the most accurate predictive models were those trained with extrinsic variables, obtaining accuracy values of 70% for urinary incontinence, 77% for urinary incontinence frequency, 71% for urinary incontinence intensity and 93% for stress urinary incontinence.\u003Cbr>Conclusions: This research has shown that extrinsic variables are more important than intrinsic variables in predicting problems related to postpartum urinary incontinence. Therefore, although not conclusive, it opens a line of research that could confirm that the prevention of Postpartum urinary incontinence could be achieved by following healthy habits in pregnant women.\u003Cbr>Keywords\u003Cbr>Machine learning, postpartum urinary incontinence, primary prevention, obstetric labor complications\u003Cbr>Submission date: 21 March 2022; Acceptance date: 9 June 2022 |  |  |\n| Introduction and related work\u003Cbr>The International Continence Society defines urinary incontinence (UI) as any involuntary loss of urine.1 Three types of UI are distinguished: (i) stress, in which the leakage is caused by jumping, sneezing, coughing, etc., that is, by an increase in intra-abdominal pressure not compensated by the muscular activity of the perineum; (ii) urgency (or bladder hyperactivity), in which the leakage is caused by anarchic contractions of the detrusor muscle of the |  |  |\n|  | 1SALBIS Research Group, Department of Electric, Systems and Automatics Engineering, Universidad de León, León, Spain\u003Cbr>2SECOMUCI Research Group, Escuela de Ingenierías Industrial e Informática, Universidad de León, León, Spain\u003Cbr>3Faculty of Health Sciences, Universidad de León, Ponferrada, Spain 4SALBIS Research Group, Nursing and Physical Therapy Department, Universidad de León, Ponferrada, Spain\u003Cbr>Corresponding author:\u003Cbr>María Teresa García-Ordás, SECOMUCI Research Group, Escuela de Ingenierías Industrial e Informática, Universidad de León, Campus de Vegazana s/n, C.P. 24071 León, Spain.\u003Cbr>Email: [mgaro@unileon.es](mgaro@unileon.es) |  |\n\nCreative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-","cbCainJC7FVMNJRq","https://ap.wps.com/l/cbCainJC7FVMNJRq","pdf",1854162,1,13,"English","en",105,"# Abstract\n## Background\n## Objective\n## Methods\n## Results\n## Conclusions\n# Introduction and related work","[{\"question\":\"What is the main goal of this study on postpartum urinary incontinence?\",\"answer\":\"To identify the most influential intrinsic, extrinsic, and combined variables for postpartum urinary incontinence using machine learning techniques.\"},{\"question\":\"How was the dataset created and what outcomes were predicted?\",\"answer\":\"Information was collected from 93 patients (pregnant women who gave birth), and models predicted urinary incontinence, urinary incontinence frequency, urinary incontinence intensity, and stress urinary incontinence.\"},{\"question\":\"Which variable set produced the most accurate predictive models?\",\"answer\":\"Models trained with extrinsic variables achieved the highest accuracies across the studied outcomes, indicating extrinsic factors are more important than intrinsic ones for prediction.\"}]","Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques | 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is the main goal of this study on postpartum urinary incontinence?","Question",{"text":75,"@type":76},"To identify the most influential intrinsic, extrinsic, and combined variables for postpartum urinary incontinence using machine learning techniques.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the dataset created and what outcomes were predicted?",{"text":80,"@type":76},"Information was collected from 93 patients (pregnant women who gave birth), and models predicted urinary incontinence, urinary incontinence frequency, urinary incontinence intensity, and stress urinary incontinence.",{"name":82,"@type":73,"acceptedAnswer":83},"Which variable set produced the most accurate predictive models?",{"text":84,"@type":76},"Models trained with extrinsic variables achieved the highest accuracies across the studied outcomes, indicating extrinsic factors are more important than intrinsic ones for 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