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...
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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 |
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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. 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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> |
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language | eng |
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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 |
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