Loading…

Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques

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 Po...

Full description

Saved in:
Bibliographic Details
Published in:Digital health 2022, Vol.8, p.205520762211112-20552076221111289
Main Authors: Benítez-Andrades, José Alberto, García-Ordás, María Teresa, Álvarez-González, María, Leirós-Rodríguez, Raquel, López Rodríguez, Ana F
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c509t-3274a1c38b3a32264f5a157ee26ab80c6696034be0da6920e10bc360a7b95a823
cites cdi_FETCH-LOGICAL-c509t-3274a1c38b3a32264f5a157ee26ab80c6696034be0da6920e10bc360a7b95a823
container_end_page 20552076221111289
container_issue
container_start_page 205520762211112
container_title Digital health
container_volume 8
creator Benítez-Andrades, José Alberto
García-Ordás, María Teresa
Álvarez-González, María
Leirós-Rodríguez, Raquel
López Rodríguez, Ana F
description 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. 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. 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. 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. 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.
doi_str_mv 10.1177/20552076221111289
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_a097b138736041aabd9766714f4dd459</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sage_id>10.1177_20552076221111289</sage_id><doaj_id>oai_doaj_org_article_a097b138736041aabd9766714f4dd459</doaj_id><sourcerecordid>2758349686</sourcerecordid><originalsourceid>FETCH-LOGICAL-c509t-3274a1c38b3a32264f5a157ee26ab80c6696034be0da6920e10bc360a7b95a823</originalsourceid><addsrcrecordid>eNp1Uk1r3DAQNaUhCWl-QG6CXnrZVB-2ZF0KJf0KBHppzmIsj3e1yJIr2QuF_PjK2dA2LdVF4s17b0YzU1VXjF4zptRbTpuGUyU5Z-XwVr-ozldss4Iv_3ifVZc57ymlTAmlmTytzkTTCl6r5rx6-IAz2tnFQOJA5h2SMeaZuDD4BcPswJMDJAedx0yGmMiU8LAGwpZMhTlBmpeRLMkFSD-KzsY1iMEiWfLKGsHuCkA8QgorUPLtgvu-YH5VnQzgM14-3RfV_aeP326-bO6-fr69eX-3sQ3V80ZwVQOzou0ECM5lPTTAGoXIJXQttVJqSUXdIe1Bak6R0c4KSUF1uoGWi4vq9ujbR9ibKbmx1GoiOPMIxLQ15RvOejRAteqYaFXR1wyg67WSUrF6qPu-bnTxenf0mpZuxN6WXiTwz0yfR4LbmW08GM3VOpJi8ObJIMW1CbMZXbboPQSMSzZctrok1I91v_6Luo9LCqVVhqsywlrLVhYWO7JsijknHH4Vw6hZV8X8sypFc33UZNjib9f_C34CIMi-EQ</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2758349686</pqid></control><display><type>article</type><title>Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques</title><source>Publicly Available Content Database</source><source>PubMed Central</source><source>SAGE Journals</source><creator>Benítez-Andrades, José Alberto ; García-Ordás, María Teresa ; Álvarez-González, María ; Leirós-Rodríguez, Raquel ; López Rodríguez, Ana F</creator><creatorcontrib>Benítez-Andrades, José Alberto ; García-Ordás, María Teresa ; Álvarez-González, María ; Leirós-Rodríguez, Raquel ; López Rodríguez, Ana F</creatorcontrib><description>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. 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. 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. 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. 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.</description><identifier>ISSN: 2055-2076</identifier><identifier>EISSN: 2055-2076</identifier><identifier>DOI: 10.1177/20552076221111289</identifier><identifier>PMID: 35832475</identifier><language>eng</language><publisher>London, England: SAGE Publications</publisher><subject>Machine learning ; Original Research ; Urinary incontinence</subject><ispartof>Digital health, 2022, Vol.8, p.205520762211112-20552076221111289</ispartof><rights>The Author(s) 2022</rights><rights>The Author(s) 2022. This work is licensed under the Creative Commons Attribution – Non-Commercial License https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2022 2022 SAGE Publications Ltd, unless otherwise noted. Manuscript content on this site is licensed under Creative Commons Licenses</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c509t-3274a1c38b3a32264f5a157ee26ab80c6696034be0da6920e10bc360a7b95a823</citedby><cites>FETCH-LOGICAL-c509t-3274a1c38b3a32264f5a157ee26ab80c6696034be0da6920e10bc360a7b95a823</cites><orcidid>0000-0002-3796-3949 ; 0000-0002-4450-349X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9272055/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2758349686?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,4024,21966,25753,27853,27923,27924,27925,37012,37013,44590,44945,45333,53791,53793</link.rule.ids></links><search><creatorcontrib>Benítez-Andrades, José Alberto</creatorcontrib><creatorcontrib>García-Ordás, María Teresa</creatorcontrib><creatorcontrib>Álvarez-González, María</creatorcontrib><creatorcontrib>Leirós-Rodríguez, Raquel</creatorcontrib><creatorcontrib>López Rodríguez, Ana F</creatorcontrib><title>Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques</title><title>Digital health</title><description>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. 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. 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. 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. 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.</description><subject>Machine learning</subject><subject>Original Research</subject><subject>Urinary incontinence</subject><issn>2055-2076</issn><issn>2055-2076</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>AFRWT</sourceid><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp1Uk1r3DAQNaUhCWl-QG6CXnrZVB-2ZF0KJf0KBHppzmIsj3e1yJIr2QuF_PjK2dA2LdVF4s17b0YzU1VXjF4zptRbTpuGUyU5Z-XwVr-ozldss4Iv_3ifVZc57ymlTAmlmTytzkTTCl6r5rx6-IAz2tnFQOJA5h2SMeaZuDD4BcPswJMDJAedx0yGmMiU8LAGwpZMhTlBmpeRLMkFSD-KzsY1iMEiWfLKGsHuCkA8QgorUPLtgvu-YH5VnQzgM14-3RfV_aeP326-bO6-fr69eX-3sQ3V80ZwVQOzou0ECM5lPTTAGoXIJXQttVJqSUXdIe1Bak6R0c4KSUF1uoGWi4vq9ujbR9ibKbmx1GoiOPMIxLQ15RvOejRAteqYaFXR1wyg67WSUrF6qPu-bnTxenf0mpZuxN6WXiTwz0yfR4LbmW08GM3VOpJi8ObJIMW1CbMZXbboPQSMSzZctrok1I91v_6Luo9LCqVVhqsywlrLVhYWO7JsijknHH4Vw6hZV8X8sypFc33UZNjib9f_C34CIMi-EQ</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Benítez-Andrades, José Alberto</creator><creator>García-Ordás, María Teresa</creator><creator>Álvarez-González, María</creator><creator>Leirós-Rodríguez, Raquel</creator><creator>López Rodríguez, Ana F</creator><general>SAGE Publications</general><general>Sage Publications Ltd</general><general>SAGE Publishing</general><scope>AFRWT</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-3796-3949</orcidid><orcidid>https://orcid.org/0000-0002-4450-349X</orcidid></search><sort><creationdate>2022</creationdate><title>Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques</title><author>Benítez-Andrades, José Alberto ; García-Ordás, María Teresa ; Álvarez-González, María ; Leirós-Rodríguez, Raquel ; López Rodríguez, Ana F</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c509t-3274a1c38b3a32264f5a157ee26ab80c6696034be0da6920e10bc360a7b95a823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Machine learning</topic><topic>Original Research</topic><topic>Urinary incontinence</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Benítez-Andrades, José Alberto</creatorcontrib><creatorcontrib>García-Ordás, María Teresa</creatorcontrib><creatorcontrib>Álvarez-González, María</creatorcontrib><creatorcontrib>Leirós-Rodríguez, Raquel</creatorcontrib><creatorcontrib>López Rodríguez, Ana F</creatorcontrib><collection>SAGE Journals</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Digital health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Benítez-Andrades, José Alberto</au><au>García-Ordás, María Teresa</au><au>Álvarez-González, María</au><au>Leirós-Rodríguez, Raquel</au><au>López Rodríguez, Ana F</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques</atitle><jtitle>Digital health</jtitle><date>2022</date><risdate>2022</risdate><volume>8</volume><spage>205520762211112</spage><epage>20552076221111289</epage><pages>205520762211112-20552076221111289</pages><issn>2055-2076</issn><eissn>2055-2076</eissn><abstract>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. 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. 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. 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. 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.</abstract><cop>London, England</cop><pub>SAGE Publications</pub><pmid>35832475</pmid><doi>10.1177/20552076221111289</doi><orcidid>https://orcid.org/0000-0002-3796-3949</orcidid><orcidid>https://orcid.org/0000-0002-4450-349X</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2055-2076
ispartof Digital health, 2022, Vol.8, p.205520762211112-20552076221111289
issn 2055-2076
2055-2076
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_a097b138736041aabd9766714f4dd459
source Publicly Available Content Database; PubMed Central; SAGE Journals
subjects Machine learning
Original Research
Urinary incontinence
title Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T06%3A07%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Detection%20of%20the%20most%20influential%20variables%20for%20preventing%20postpartum%20urinary%20incontinence%20using%20machine%20learning%20techniques&rft.jtitle=Digital%20health&rft.au=Ben%C3%ADtez-Andrades,%20Jos%C3%A9%20Alberto&rft.date=2022&rft.volume=8&rft.spage=205520762211112&rft.epage=20552076221111289&rft.pages=205520762211112-20552076221111289&rft.issn=2055-2076&rft.eissn=2055-2076&rft_id=info:doi/10.1177/20552076221111289&rft_dat=%3Cproquest_doaj_%3E2758349686%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c509t-3274a1c38b3a32264f5a157ee26ab80c6696034be0da6920e10bc360a7b95a823%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2758349686&rft_id=info:pmid/35832475&rft_sage_id=10.1177_20552076221111289&rfr_iscdi=true