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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...
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Published in: | Digital health 2022, Vol.8, p.205520762211112-20552076221111289 |
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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. |
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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 & 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 & Medical Complete (Alumni)</collection><collection>Health & 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> |
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subjects | Machine learning Original Research Urinary incontinence |
title | Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques |
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