Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks
Artificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. This technique has been used for prediction and classification problems in many areas of medical signal processing. The aim of this paper was to identify individuals with high ri...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/8953083 |
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| _version_ | 1832568457972940800 |
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| author | Tatjana Gligorijević Zoran Ševarac Branislav Milovanović Vlado Đajić Marija Zdravković Saša Hinić Marina Arsić Milica Aleksić |
| author_facet | Tatjana Gligorijević Zoran Ševarac Branislav Milovanović Vlado Đajić Marija Zdravković Saša Hinić Marina Arsić Milica Aleksić |
| author_sort | Tatjana Gligorijević |
| collection | DOAJ |
| description | Artificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. This technique has been used for prediction and classification problems in many areas of medical signal processing. The aim of this paper was to identify individuals with high risk of death after acute myocardial infarction using ANN. A training dataset for ANN was 1705 consecutive patients who underwent 24-hour ECG monitoring, short ECG analysis, noninvasive beat-to-beat heart-rate variability, and baroreflex sensitivity that were followed for 3 years. The proposed neural network classifier showed good performance for survival prediction: 88% accuracy, 81% sensitivity, 93% specificity, 0.85 F-measure, and area under the curve value of 0.77. These findings support the theory that patients with high sympathetic activity (reduced baroreflex sensitivity) have an increased risk of mortality independent of other risk factors and that artificial neural networks can indicate the individuals with a higher risk. |
| format | Article |
| id | doaj-art-c76ce0e4064c4787ba16e9e301d65e72 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c76ce0e4064c4787ba16e9e301d65e722025-02-03T00:59:02ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/89530838953083Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural NetworksTatjana Gligorijević0Zoran Ševarac1Branislav Milovanović2Vlado Đajić3Marija Zdravković4Saša Hinić5Marina Arsić6Milica Aleksić7Department of Cardiology, University Hospital Medical Center Bežanijska Kosa, 11080 Belgrade, SerbiaFaculty of Organizational Sciences, University of Belgrade, 11000 Belgrade, SerbiaDepartment of Cardiology, University Hospital Medical Center Bežanijska Kosa, 11080 Belgrade, SerbiaDepartment of Neurology, University Clinical Center of the Republic of Srpska, 78000 Banjaluka, Bosnia and HerzegovinaDepartment of Cardiology, University Hospital Medical Center Bežanijska Kosa, 11080 Belgrade, SerbiaDepartment of Cardiology, University Hospital Medical Center Bežanijska Kosa, 11080 Belgrade, SerbiaDepartment of Cardiology, University Hospital Medical Center Bežanijska Kosa, 11080 Belgrade, SerbiaDepartment of Cardiology, University Hospital Medical Center Bežanijska Kosa, 11080 Belgrade, SerbiaArtificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. This technique has been used for prediction and classification problems in many areas of medical signal processing. The aim of this paper was to identify individuals with high risk of death after acute myocardial infarction using ANN. A training dataset for ANN was 1705 consecutive patients who underwent 24-hour ECG monitoring, short ECG analysis, noninvasive beat-to-beat heart-rate variability, and baroreflex sensitivity that were followed for 3 years. The proposed neural network classifier showed good performance for survival prediction: 88% accuracy, 81% sensitivity, 93% specificity, 0.85 F-measure, and area under the curve value of 0.77. These findings support the theory that patients with high sympathetic activity (reduced baroreflex sensitivity) have an increased risk of mortality independent of other risk factors and that artificial neural networks can indicate the individuals with a higher risk.http://dx.doi.org/10.1155/2017/8953083 |
| spellingShingle | Tatjana Gligorijević Zoran Ševarac Branislav Milovanović Vlado Đajić Marija Zdravković Saša Hinić Marina Arsić Milica Aleksić Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks Complexity |
| title | Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks |
| title_full | Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks |
| title_fullStr | Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks |
| title_full_unstemmed | Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks |
| title_short | Follow-Up and Risk Assessment in Patients with Myocardial Infarction Using Artificial Neural Networks |
| title_sort | follow up and risk assessment in patients with myocardial infarction using artificial neural networks |
| url | http://dx.doi.org/10.1155/2017/8953083 |
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