Design and analysis of an early heart attack detection using openCV
Millions of people die every year from heart attacks, according to research. The healthcare industry generates massive volumes of data related to heart attacks, but this data is sadly not being processed for hidden insights that could improve decision-making. Early detection of heart attack symptoms...
| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37128/ http://umpir.ump.edu.my/id/eprint/37128/1/Design%20and%20analysis%20of%20an%20early%20heart%20attack%20detection%20using%20opencv.pdf |
| _version_ | 1848825173177794560 |
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| author | Muhammad Rafsanjani, Basri Fahmi, Samsuri |
| author_facet | Muhammad Rafsanjani, Basri Fahmi, Samsuri |
| author_sort | Muhammad Rafsanjani, Basri |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Millions of people die every year from heart attacks, according to research. The healthcare industry generates massive volumes of data related to heart attacks, but this data is sadly not being processed for hidden insights that could improve decision-making. Early detection of heart attack symptoms is a crucial part of treatment at the moment. Numerous researchers, each applying their own unique machine learning approach, have used the UCI machine learning heart attack dataset. This research aims to detect cardiac events with the use of four different algorithms: logistic regression, decision trees, random forest, and k nearest neighbor using python language. Next, in this project, website prediction of the heart attack prediction are build using python and flask framework. Hyper-parameter tuning method also has been applied to see does the algorithm increase accuracy or not. |
| first_indexed | 2025-11-15T03:24:43Z |
| format | Conference or Workshop Item |
| id | ump-37128 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:24:43Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-371282023-03-14T08:18:04Z http://umpir.ump.edu.my/id/eprint/37128/ Design and analysis of an early heart attack detection using openCV Muhammad Rafsanjani, Basri Fahmi, Samsuri T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Millions of people die every year from heart attacks, according to research. The healthcare industry generates massive volumes of data related to heart attacks, but this data is sadly not being processed for hidden insights that could improve decision-making. Early detection of heart attack symptoms is a crucial part of treatment at the moment. Numerous researchers, each applying their own unique machine learning approach, have used the UCI machine learning heart attack dataset. This research aims to detect cardiac events with the use of four different algorithms: logistic regression, decision trees, random forest, and k nearest neighbor using python language. Next, in this project, website prediction of the heart attack prediction are build using python and flask framework. Hyper-parameter tuning method also has been applied to see does the algorithm increase accuracy or not. 2022-11-15 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37128/1/Design%20and%20analysis%20of%20an%20early%20heart%20attack%20detection%20using%20opencv.pdf Muhammad Rafsanjani, Basri and Fahmi, Samsuri (2022) Design and analysis of an early heart attack detection using openCV. In: The 6th National Conference for Postgraduate Research (NCON-PGR 2022) , 15 November 2022 , Virtual Conference, Universiti Malaysia Pahang, Malaysia. p. 163.. (Published) https://ncon-pgr.ump.edu.my/index.php/en/?option=com_fileman&view=file&routed=1&name=E-BOOK%20NCON%202022%20.pdf&folder=E-BOOK%20NCON%202022&container=fileman-files |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Muhammad Rafsanjani, Basri Fahmi, Samsuri Design and analysis of an early heart attack detection using openCV |
| title | Design and analysis of an early heart attack detection using openCV |
| title_full | Design and analysis of an early heart attack detection using openCV |
| title_fullStr | Design and analysis of an early heart attack detection using openCV |
| title_full_unstemmed | Design and analysis of an early heart attack detection using openCV |
| title_short | Design and analysis of an early heart attack detection using openCV |
| title_sort | design and analysis of an early heart attack detection using opencv |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/37128/ http://umpir.ump.edu.my/id/eprint/37128/ http://umpir.ump.edu.my/id/eprint/37128/1/Design%20and%20analysis%20of%20an%20early%20heart%20attack%20detection%20using%20opencv.pdf |