Effective heart disease prediction using hybrid machine learning techniques

Heart is the most effective and important part of our body. And in this era, heart disease became one of the leading causes for death. The projection of cardiovascular disease is most challenging tasks in the fields of healthcare. So, we need to have an ideology to develop an application which can h...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Anjaneyulu, M., Degala, Divya Priya, Devika, P., Hema, V.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 1
container_start_page
container_title
container_volume 2492
creator Anjaneyulu, M.
Degala, Divya Priya
Devika, P.
Hema, V.
description Heart is the most effective and important part of our body. And in this era, heart disease became one of the leading causes for death. The projection of cardiovascular disease is most challenging tasks in the fields of healthcare. So, we need to have an ideology to develop an application which can help people to find out about the occurrence of any heart related diseases in the early stages so as to prevent the harm. It is not ideal for a person to practically undergo lots of tests that are high in cost just to make sure that they are healthy. In order to avoid it we put forward a methodology where a person can detect any heart related diseases in early stages only. Here, we provided certain basic parameters like age, gender, pulse rate and more. And also, we used few machine learning techniques such as SVM, LR, NB, ANN, HRFLM and Extension Extreme Algorithms. Finally, we compare all the algorithms to get best accuracy.
doi_str_mv 10.1063/5.0114370
format Conference Proceeding
fullrecord <record><control><sourceid>proquest_scita</sourceid><recordid>TN_cdi_proquest_journals_2817114544</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2817114544</sourcerecordid><originalsourceid>FETCH-LOGICAL-c208t-b5d5505aaeec7c5be8ec7d4a5a288c2f0f20906e15c82421315acd2f79204033</originalsourceid><addsrcrecordid>eNp9kEtLAzEUhYMoWKsL_0HAnTD15jXJLKXUBxbcdOEupHk4Ke3MmEwL_ffO0II7Vwfu_e49nIPQPYEZgZI9iRkQwpmECzQhQpBClqS8RBOAiheUs69rdJPzBoBWUqoJ-liE4G0fDx7X3qQeu5i9yR53ybs4LNoG73NsvnF9XKfo8M7YOjYebwe6Gee9t3UTf_Y-36KrYLbZ3511ilYvi9X8rVh-vr7Pn5eFpaD6Yi2cECCM8d5KK9ZeDeq4EYYqZWmAQKGC0hNhFeWUMCKMdTTIigIHxqbo4fS2S-1o2-tNu0_N4KipInKILzgfqMcTlW3szZhDdynuTDrqQ5u00OeedOfCfzABPRb7d8B-ARe-alo</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype><pqid>2817114544</pqid></control><display><type>conference_proceeding</type><title>Effective heart disease prediction using hybrid machine learning techniques</title><source>AIP Journals Complete</source><creator>Anjaneyulu, M. ; Degala, Divya Priya ; Devika, P. ; Hema, V.</creator><contributor>Reddy, M Venkateswar ; Gupta, M Satyanarayana ; Anand, A Vivek</contributor><creatorcontrib>Anjaneyulu, M. ; Degala, Divya Priya ; Devika, P. ; Hema, V. ; Reddy, M Venkateswar ; Gupta, M Satyanarayana ; Anand, A Vivek</creatorcontrib><description>Heart is the most effective and important part of our body. And in this era, heart disease became one of the leading causes for death. The projection of cardiovascular disease is most challenging tasks in the fields of healthcare. So, we need to have an ideology to develop an application which can help people to find out about the occurrence of any heart related diseases in the early stages so as to prevent the harm. It is not ideal for a person to practically undergo lots of tests that are high in cost just to make sure that they are healthy. In order to avoid it we put forward a methodology where a person can detect any heart related diseases in early stages only. Here, we provided certain basic parameters like age, gender, pulse rate and more. And also, we used few machine learning techniques such as SVM, LR, NB, ANN, HRFLM and Extension Extreme Algorithms. Finally, we compare all the algorithms to get best accuracy.</description><identifier>ISSN: 0094-243X</identifier><identifier>EISSN: 1551-7616</identifier><identifier>DOI: 10.1063/5.0114370</identifier><identifier>CODEN: APCPCS</identifier><language>eng</language><publisher>Melville: American Institute of Physics</publisher><subject>Algorithms ; Cardiovascular disease ; Heart ; Heart diseases ; Machine learning ; Pulse rate</subject><ispartof>AIP conference proceedings, 2023, Vol.2492 (1)</ispartof><rights>Author(s)</rights><rights>2023 Author(s). Published by AIP Publishing.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c208t-b5d5505aaeec7c5be8ec7d4a5a288c2f0f20906e15c82421315acd2f79204033</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubs.aip.org/acp/article-lookup/doi/10.1063/5.0114370$$EHTML$$P50$$Gscitation$$H</linktohtml><link.rule.ids>309,310,314,776,780,785,786,790,4498,23909,23910,25118,27901,27902,76127</link.rule.ids></links><search><contributor>Reddy, M Venkateswar</contributor><contributor>Gupta, M Satyanarayana</contributor><contributor>Anand, A Vivek</contributor><creatorcontrib>Anjaneyulu, M.</creatorcontrib><creatorcontrib>Degala, Divya Priya</creatorcontrib><creatorcontrib>Devika, P.</creatorcontrib><creatorcontrib>Hema, V.</creatorcontrib><title>Effective heart disease prediction using hybrid machine learning techniques</title><title>AIP conference proceedings</title><description>Heart is the most effective and important part of our body. And in this era, heart disease became one of the leading causes for death. The projection of cardiovascular disease is most challenging tasks in the fields of healthcare. So, we need to have an ideology to develop an application which can help people to find out about the occurrence of any heart related diseases in the early stages so as to prevent the harm. It is not ideal for a person to practically undergo lots of tests that are high in cost just to make sure that they are healthy. In order to avoid it we put forward a methodology where a person can detect any heart related diseases in early stages only. Here, we provided certain basic parameters like age, gender, pulse rate and more. And also, we used few machine learning techniques such as SVM, LR, NB, ANN, HRFLM and Extension Extreme Algorithms. Finally, we compare all the algorithms to get best accuracy.</description><subject>Algorithms</subject><subject>Cardiovascular disease</subject><subject>Heart</subject><subject>Heart diseases</subject><subject>Machine learning</subject><subject>Pulse rate</subject><issn>0094-243X</issn><issn>1551-7616</issn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNp9kEtLAzEUhYMoWKsL_0HAnTD15jXJLKXUBxbcdOEupHk4Ke3MmEwL_ffO0II7Vwfu_e49nIPQPYEZgZI9iRkQwpmECzQhQpBClqS8RBOAiheUs69rdJPzBoBWUqoJ-liE4G0fDx7X3qQeu5i9yR53ybs4LNoG73NsvnF9XKfo8M7YOjYebwe6Gee9t3UTf_Y-36KrYLbZ3511ilYvi9X8rVh-vr7Pn5eFpaD6Yi2cECCM8d5KK9ZeDeq4EYYqZWmAQKGC0hNhFeWUMCKMdTTIigIHxqbo4fS2S-1o2-tNu0_N4KipInKILzgfqMcTlW3szZhDdynuTDrqQ5u00OeedOfCfzABPRb7d8B-ARe-alo</recordid><startdate>20230522</startdate><enddate>20230522</enddate><creator>Anjaneyulu, M.</creator><creator>Degala, Divya Priya</creator><creator>Devika, P.</creator><creator>Hema, V.</creator><general>American Institute of Physics</general><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>20230522</creationdate><title>Effective heart disease prediction using hybrid machine learning techniques</title><author>Anjaneyulu, M. ; Degala, Divya Priya ; Devika, P. ; Hema, V.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c208t-b5d5505aaeec7c5be8ec7d4a5a288c2f0f20906e15c82421315acd2f79204033</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Cardiovascular disease</topic><topic>Heart</topic><topic>Heart diseases</topic><topic>Machine learning</topic><topic>Pulse rate</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Anjaneyulu, M.</creatorcontrib><creatorcontrib>Degala, Divya Priya</creatorcontrib><creatorcontrib>Devika, P.</creatorcontrib><creatorcontrib>Hema, V.</creatorcontrib><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Anjaneyulu, M.</au><au>Degala, Divya Priya</au><au>Devika, P.</au><au>Hema, V.</au><au>Reddy, M Venkateswar</au><au>Gupta, M Satyanarayana</au><au>Anand, A Vivek</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Effective heart disease prediction using hybrid machine learning techniques</atitle><btitle>AIP conference proceedings</btitle><date>2023-05-22</date><risdate>2023</risdate><volume>2492</volume><issue>1</issue><issn>0094-243X</issn><eissn>1551-7616</eissn><coden>APCPCS</coden><abstract>Heart is the most effective and important part of our body. And in this era, heart disease became one of the leading causes for death. The projection of cardiovascular disease is most challenging tasks in the fields of healthcare. So, we need to have an ideology to develop an application which can help people to find out about the occurrence of any heart related diseases in the early stages so as to prevent the harm. It is not ideal for a person to practically undergo lots of tests that are high in cost just to make sure that they are healthy. In order to avoid it we put forward a methodology where a person can detect any heart related diseases in early stages only. Here, we provided certain basic parameters like age, gender, pulse rate and more. And also, we used few machine learning techniques such as SVM, LR, NB, ANN, HRFLM and Extension Extreme Algorithms. Finally, we compare all the algorithms to get best accuracy.</abstract><cop>Melville</cop><pub>American Institute of Physics</pub><doi>10.1063/5.0114370</doi><tpages>6</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0094-243X
ispartof AIP conference proceedings, 2023, Vol.2492 (1)
issn 0094-243X
1551-7616
language eng
recordid cdi_proquest_journals_2817114544
source AIP Journals Complete
subjects Algorithms
Cardiovascular disease
Heart
Heart diseases
Machine learning
Pulse rate
title Effective heart disease prediction using hybrid machine learning techniques
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T23%3A04%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_scita&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Effective%20heart%20disease%20prediction%20using%20hybrid%20machine%20learning%20techniques&rft.btitle=AIP%20conference%20proceedings&rft.au=Anjaneyulu,%20M.&rft.date=2023-05-22&rft.volume=2492&rft.issue=1&rft.issn=0094-243X&rft.eissn=1551-7616&rft.coden=APCPCS&rft_id=info:doi/10.1063/5.0114370&rft_dat=%3Cproquest_scita%3E2817114544%3C/proquest_scita%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2817114544&rft_id=info:pmid/&rfr_iscdi=true