The Cardiovascular Disease Prediction Using Machine Learning
Because of technology developments, the ECG yields improved outcomes in the realm of biomedical science and research. The Electrocardiogram reveals basic the heart's electrical activity. Early detection of aberrant heart disorders is crucial for diagnosing cardiac problems and averting sudden c...
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| Format: | Article |
| Language: | English |
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Universitas Buana Perjuangan Karawang
2023-01-01
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| Series: | Buana Information Technology and Computer Sciences |
| Subjects: | |
| Online Access: | https://journal.ubpkarawang.ac.id/index.php/bit-cs/article/view/3060 |
| _version_ | 1811174128512663552 |
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| author | Shivam Pandey |
| author_facet | Shivam Pandey |
| author_sort | Shivam Pandey |
| collection | DOAJ |
| description | Because of technology developments, the ECG yields improved outcomes in the realm of biomedical science and research. The Electrocardiogram reveals basic the heart's electrical activity. Early detection of aberrant heart disorders is crucial for diagnosing cardiac problems and averting sudden cardiac deaths. Measurements on an electrocardiogram (ECG) among people with comparable cardiac issues are essentially equal. Analyzing the Electrocardiogram characteristics can help predict abnormalities. Medical professionals presently base the preponderance of their Electrocardiogram diagnosis on their unique particular areas of expertise, which places a substantial load on their shoulders and reduces their performance. The use of technology that automatically analyses ECGs as hospital personnel performs their duties will be advantageous. A suitable algorithm must be able to categories Input signal with uncertain awesome feature on just how much they approximate Input signal having known characteristics in order to speed up the identification of heart illnesses. A possibility of identifying a tachycardia is raised if this predictor can reliably recognize connections, and this technique may be helpful in lab settings. To accurately diagnose myocardial illness, a powerful machine learning technique should be used. Through using recommended method, the effectiveness of cardiovascular disease identification using ECG dataset was evaluated. The reliability, sensitivities, and validity obtained using the Svm algorithm were 99.314%, 97.60%, and 97.60% respectively. |
| first_indexed | 2024-04-10T17:58:10Z |
| format | Article |
| id | doaj.art-3e36ec3343cd413a85f21b141ebf31af |
| institution | Directory Open Access Journal |
| issn | 2715-2448 2715-7199 |
| language | English |
| last_indexed | 2024-04-10T17:58:10Z |
| publishDate | 2023-01-01 |
| publisher | Universitas Buana Perjuangan Karawang |
| record_format | Article |
| series | Buana Information Technology and Computer Sciences |
| spelling | doaj.art-3e36ec3343cd413a85f21b141ebf31af2023-02-02T16:45:17ZengUniversitas Buana Perjuangan KarawangBuana Information Technology and Computer Sciences2715-24482715-71992023-01-0141242710.36805/bit-cs.v4i1.30603060The Cardiovascular Disease Prediction Using Machine LearningShivam Pandey0ChanBecause of technology developments, the ECG yields improved outcomes in the realm of biomedical science and research. The Electrocardiogram reveals basic the heart's electrical activity. Early detection of aberrant heart disorders is crucial for diagnosing cardiac problems and averting sudden cardiac deaths. Measurements on an electrocardiogram (ECG) among people with comparable cardiac issues are essentially equal. Analyzing the Electrocardiogram characteristics can help predict abnormalities. Medical professionals presently base the preponderance of their Electrocardiogram diagnosis on their unique particular areas of expertise, which places a substantial load on their shoulders and reduces their performance. The use of technology that automatically analyses ECGs as hospital personnel performs their duties will be advantageous. A suitable algorithm must be able to categories Input signal with uncertain awesome feature on just how much they approximate Input signal having known characteristics in order to speed up the identification of heart illnesses. A possibility of identifying a tachycardia is raised if this predictor can reliably recognize connections, and this technique may be helpful in lab settings. To accurately diagnose myocardial illness, a powerful machine learning technique should be used. Through using recommended method, the effectiveness of cardiovascular disease identification using ECG dataset was evaluated. The reliability, sensitivities, and validity obtained using the Svm algorithm were 99.314%, 97.60%, and 97.60% respectively.https://journal.ubpkarawang.ac.id/index.php/bit-cs/article/view/3060machine learningcardiovascular diseaseheart diseasecomputer science |
| spellingShingle | Shivam Pandey The Cardiovascular Disease Prediction Using Machine Learning Buana Information Technology and Computer Sciences machine learning cardiovascular disease heart disease computer science |
| title | The Cardiovascular Disease Prediction Using Machine Learning |
| title_full | The Cardiovascular Disease Prediction Using Machine Learning |
| title_fullStr | The Cardiovascular Disease Prediction Using Machine Learning |
| title_full_unstemmed | The Cardiovascular Disease Prediction Using Machine Learning |
| title_short | The Cardiovascular Disease Prediction Using Machine Learning |
| title_sort | cardiovascular disease prediction using machine learning |
| topic | machine learning cardiovascular disease heart disease computer science |
| url | https://journal.ubpkarawang.ac.id/index.php/bit-cs/article/view/3060 |
| work_keys_str_mv | AT shivampandey thecardiovasculardiseasepredictionusingmachinelearning AT shivampandey cardiovasculardiseasepredictionusingmachinelearning |