Comparing and Predicting Hepatic Encephalopathy Complications Using Random Forest Algorithm in Active Men
Purpose: Liver diseases are among the most common disorders worldwide. For liver transplant patients, the presence of postoperative problems increases the complexity of postoperative nursing. Patients’ hospitalization is prolonged, and the costs of hospitalization increase. Data mining has become an...
Uložené v:
| Vydané v: | Physical treatments Ročník 15; číslo 1; s. 81 - 90 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Negah Institute for Scientific Communication
01.01.2025
|
| Predmet: | |
| ISSN: | 2423-5830, 2423-5830 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Purpose: Liver diseases are among the most common disorders worldwide. For liver transplant patients, the presence of postoperative problems increases the complexity of postoperative nursing. Patients’ hospitalization is prolonged, and the costs of hospitalization increase. Data mining has become an easy way to predict diseases in recent years. Accordingly, this study compares and predicts the complications of hepatic encephalopathy in active and inactive men after liver transplantation. Methods: The statistical population of this study was 852 people. Among them, 350 active men (162 healthy people and 188 people with encephalopathy symptoms) and 402 inactive men (210 healthy people and 192 people with encephalopathy symptoms) were selected as study subjects. These people underwent a liver transplant in the hospital between 2010 and 2011. The random forest algorithm and 14 features from laboratory records were used to predict encephalopathy complications after liver transplantation. Meanwhile, MATLAB software, version 2023, was used for data analysis. Results: There was no significant difference in predicting encephalopathy complications by random forest algorithm between active and inactive men. Also, this study showed that the random forest algorithm using 14 features is 76.2% and 75.5% accurate for diagnosing hepatic encephalopathy after liver transplantation in active and inactive men, respectively. Conclusion: Computer-based decision support systems can help to reduce poor healthcare decisions and the expenses associated with unneeded clinical trials in both active and inactive populations. Based on the accuracy of the random forest algorithm on the data, this system can assist clinicians in forecasting the risk of hepatic encephalopathy following transplantation with high accuracy and at a cheap cost. |
|---|---|
| AbstractList | Purpose: Liver diseases are among the most common disorders worldwide. For liver transplant patients, the presence of postoperative problems increases the complexity of postoperative nursing. Patients’ hospitalization is prolonged, and the costs of hospitalization increase. Data mining has become an easy way to predict diseases in recent years. Accordingly, this study compares and predicts the complications of hepatic encephalopathy in active and inactive men after liver transplantation. Methods: The statistical population of this study was 852 people. Among them, 350 active men (162 healthy people and 188 people with encephalopathy symptoms) and 402 inactive men (210 healthy people and 192 people with encephalopathy symptoms) were selected as study subjects. These people underwent a liver transplant in the hospital between 2010 and 2011. The random forest algorithm and 14 features from laboratory records were used to predict encephalopathy complications after liver transplantation. Meanwhile, MATLAB software, version 2023, was used for data analysis. Results: There was no significant difference in predicting encephalopathy complications by random forest algorithm between active and inactive men. Also, this study showed that the random forest algorithm using 14 features is 76.2% and 75.5% accurate for diagnosing hepatic encephalopathy after liver transplantation in active and inactive men, respectively. Conclusion: Computer-based decision support systems can help to reduce poor healthcare decisions and the expenses associated with unneeded clinical trials in both active and inactive populations. Based on the accuracy of the random forest algorithm on the data, this system can assist clinicians in forecasting the risk of hepatic encephalopathy following transplantation with high accuracy and at a cheap cost. |
| Author | Fasihi, Ahmad Fasihi, Leila Eslami, Rasoul Tartibian, Bakhtyar |
| Author_xml | – sequence: 1 givenname: Bakhtyar surname: Tartibian fullname: Tartibian, Bakhtyar – sequence: 2 givenname: Leila surname: Fasihi fullname: Fasihi, Leila – sequence: 3 givenname: Rasoul surname: Eslami fullname: Eslami, Rasoul – sequence: 4 givenname: Ahmad surname: Fasihi fullname: Fasihi, Ahmad |
| BookMark | eNpNkMtqAjEUhkOxUGtdd5sXGM3VSZYiWgVLS6nrkEkyGplJhsxQ6Ns3aindnOt_Pjj_IxiFGBwAzxjNKOFSzLvhPMN8hmcLluMdGBNGaMEFRaN_9QOY9v0ZIURKwXBZjoFfxbbTyYcj1MHC9-SsN8Ol3bpOD97AdTCuO-km5vb0DS_6xpu8iqGHh_4i_cinsYWbmFw_wGVzjMkPpxb6AJcZ9uXgqwtP4L7WTe-mv3kCDpv152pb7N9edqvlvjAES1xojRfSCV7hyhlipBAMUUQqKiyjDlXYSFRZISRF2mBaZj1jupZ1aQV2laETsLtxbdRn1SXf6vStovbqOojpqHTKjzVOSccsK0skkSbMcSpQVVpb15RKzgjnmTW_sUyKfZ9c_cfDSF2NV9l4hbnCKhuvMP0Bu8l5Bg |
| ContentType | Journal Article |
| DBID | AAYXX CITATION DOA |
| DOI | 10.32598/ptj.15.1.645.1 |
| DatabaseName | CrossRef DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: DOA name: Open Access: DOAJ - Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| EISSN | 2423-5830 |
| EndPage | 90 |
| ExternalDocumentID | oai_doaj_org_article_9e4d477090a24e5380b7ddff33954255 10_32598_ptj_15_1_645_1 |
| GroupedDBID | AAYXX ALMA_UNASSIGNED_HOLDINGS CITATION GROUPED_DOAJ M~E |
| ID | FETCH-LOGICAL-c2191-aa169e85b1bec2c98840302b38d43e0b1c90bd88930ac137aa144af9f7d81ebc3 |
| IEDL.DBID | DOA |
| ISSN | 2423-5830 |
| IngestDate | Fri Oct 03 12:52:41 EDT 2025 Sat Nov 29 08:05:21 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2191-aa169e85b1bec2c98840302b38d43e0b1c90bd88930ac137aa144af9f7d81ebc3 |
| OpenAccessLink | https://doaj.org/article/9e4d477090a24e5380b7ddff33954255 |
| PageCount | 10 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_9e4d477090a24e5380b7ddff33954255 crossref_primary_10_32598_ptj_15_1_645_1 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-01-01 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – month: 01 year: 2025 text: 2025-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | Physical treatments |
| PublicationYear | 2025 |
| Publisher | Negah Institute for Scientific Communication |
| Publisher_xml | – name: Negah Institute for Scientific Communication |
| SSID | ssj0002784177 |
| Score | 2.2783957 |
| Snippet | Purpose: Liver diseases are among the most common disorders worldwide. For liver transplant patients, the presence of postoperative problems increases the... |
| SourceID | doaj crossref |
| SourceType | Open Website Index Database |
| StartPage | 81 |
| SubjectTerms | encephalopathy liver transplant men random forest algorithm |
| Title | Comparing and Predicting Hepatic Encephalopathy Complications Using Random Forest Algorithm in Active Men |
| URI | https://doaj.org/article/9e4d477090a24e5380b7ddff33954255 |
| Volume | 15 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: Open Access: DOAJ - Directory of Open Access Journals customDbUrl: eissn: 2423-5830 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002784177 issn: 2423-5830 databaseCode: DOA dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2423-5830 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002784177 issn: 2423-5830 databaseCode: M~E dateStart: 20150101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV09T8MwELVQxcCCQIAoX_LAwJI2jp06HkvVqgOtKgSom-XYTltU0ioNSCz8ds5Oi8LEwuIhcqLonXP3Lj7fQ-iWKQjCyqhAiVQEzBoaqE5kg1hx3mEGKLvXIXt54ONxMp2KSU3qy9WEVe2BK-DawjLDOA9FqCJm4fMMU25MllEqYlhvvnspsJ5aMvW6207jvOrlQ4HiJ-11CW4hbpFWh8H4KwzVuvX7sDI4QodbPoi71Xscoz2bn6BFr1IHzGcYEn08KdxuiqtPxkPrKqA17rvDhnO1XDlF4U_cqxeGY18GgB_h1tUbdtqbmxJ3l7NVsSjnb3iR4653cnhk81P0POg_9YbBVhMh0OBbSKAU6QibxCkB8CMtEkjQaBilNDGM2jAlWoSpSYCFhEoTymE-YyoTGTcJsammZ6iRr3J7jjAkxqkGfiaMK9ZzTNBpvUAEV0ZEhugmuttBJNdV6wsJKYNHUwKaksSSSEBTkia6dxD-THM9q_0FsKTcWlL-ZcmL_3jIJTqInEKv_0lyhRpl8W6v0b7-KBeb4sYvEhhHX_1vhO7COw |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Comparing+and+Predicting+Hepatic+Encephalopathy+Complications+Using+Random+Forest+Algorithm+in+Active+Men&rft.jtitle=Physical+treatments&rft.au=Bakhtyar+Tartibian&rft.au=Leila+Fasihi&rft.au=Rasoul+Eslami&rft.au=Ahmad+Fasihi&rft.date=2025-01-01&rft.pub=Negah+Institute+for+Scientific+Communication&rft.eissn=2423-5830&rft.volume=15&rft.issue=1&rft.spage=81&rft.epage=90&rft_id=info:doi/10.32598%2Fptj.15.1.645.1&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_9e4d477090a24e5380b7ddff33954255 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2423-5830&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2423-5830&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2423-5830&client=summon |