Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation
The increasing prevalence of atrial fibrillation (AF) and its association with Major Adverse Cardiovascular Events (MACE) presents challenges in early identification and treatment. Although existing risk factors, biomarkers, genetic variants, and imaging parameters predict MACE, emerging factors may...
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MDPI AG
2024-01-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/12/2/13 |
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| author | Pedro Moltó-Balado Silvia Reverté-Villarroya Victor Alonso-Barberán Cinta Monclús-Arasa Maria Teresa Balado-Albiol Josep Clua-Queralt Josep-Lluis Clua-Espuny |
| author_facet | Pedro Moltó-Balado Silvia Reverté-Villarroya Victor Alonso-Barberán Cinta Monclús-Arasa Maria Teresa Balado-Albiol Josep Clua-Queralt Josep-Lluis Clua-Espuny |
| author_sort | Pedro Moltó-Balado |
| collection | DOAJ |
| description | The increasing prevalence of atrial fibrillation (AF) and its association with Major Adverse Cardiovascular Events (MACE) presents challenges in early identification and treatment. Although existing risk factors, biomarkers, genetic variants, and imaging parameters predict MACE, emerging factors may be more decisive. Artificial intelligence and machine learning techniques (ML) offer a promising avenue for more effective AF evolution prediction. Five ML models were developed to obtain predictors of MACE in AF patients. Two-thirds of the data were used for training, employing diverse approaches and optimizing to minimize prediction errors, while the remaining third was reserved for testing and validation. AdaBoost emerged as the top-performing model (accuracy: 0.9999; recall: 1; F1 score: 0.9997). Noteworthy features influencing predictions included the Charlson Comorbidity Index (CCI), diabetes mellitus, cancer, the Wells scale, and CHA<sub>2</sub>DS<sub>2</sub>-VASc, with specific associations identified. Elevated MACE risk was observed, with a CCI score exceeding 2.67 ± 1.31 (<i>p</i> < 0.001), CHA2DS2-VASc score of 4.62 ± 1.02 (<i>p</i> < 0.001), and an intermediate-risk Wells scale classification. Overall, the AdaBoost ML offers an alternative predictive approach to facilitate the early identification of MACE risk in the assessment of patients with AF. |
| first_indexed | 2024-03-07T22:11:40Z |
| format | Article |
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| language | English |
| last_indexed | 2024-03-07T22:11:40Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-042a9db39edc491f9f821acd12e4b1722024-02-23T15:36:13ZengMDPI AGTechnologies2227-70802024-01-011221310.3390/technologies12020013Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial FibrillationPedro Moltó-Balado0Silvia Reverté-Villarroya1Victor Alonso-Barberán2Cinta Monclús-Arasa3Maria Teresa Balado-Albiol4Josep Clua-Queralt5Josep-Lluis Clua-Espuny6Primary Health-Care Center Tortosa Oest, Institut Català de la Salut, Primary Care Service (SAP) Terres de l’Ebre, CAP Baix Ebre Avda de Colom, 16-20, 43500 Tortosa, SpainNursing Department, Advanced Nursing Research Group at Rovira I Virgili University, Biomedicine Doctoral Programme Campus Terres de l’Ebre, Av. De Remolins, 13, 43500 Tortosa, SpainInstitut d’Educació Secundària El Caminàs, C/Pintor Soler Blasco, 3, Conselleria d’Educació, 12003 Castellón, SpainPrimary Health-Care Center Tortosa Oest, Institut Català de la Salut, Primary Care Service (SAP) Terres de l’Ebre, CAP Baix Ebre Avda de Colom, 16-20, 43500 Tortosa, SpainPrimary Health-Care Center CS Borriana I, Conselleria de Sanitat, Avinguda Nules, 31, 12530 Borriana, SpainPrimary Health-Care Center EAP Tortosa Est, Institut Català de la Salut, CAP El Temple Plaça Carrilet, s/n, 43500 Tortosa, SpainPrimary Health-Care Center EAP Tortosa Est, Institut Català de la Salut, CAP El Temple Plaça Carrilet, s/n, 43500 Tortosa, SpainThe increasing prevalence of atrial fibrillation (AF) and its association with Major Adverse Cardiovascular Events (MACE) presents challenges in early identification and treatment. Although existing risk factors, biomarkers, genetic variants, and imaging parameters predict MACE, emerging factors may be more decisive. Artificial intelligence and machine learning techniques (ML) offer a promising avenue for more effective AF evolution prediction. Five ML models were developed to obtain predictors of MACE in AF patients. Two-thirds of the data were used for training, employing diverse approaches and optimizing to minimize prediction errors, while the remaining third was reserved for testing and validation. AdaBoost emerged as the top-performing model (accuracy: 0.9999; recall: 1; F1 score: 0.9997). Noteworthy features influencing predictions included the Charlson Comorbidity Index (CCI), diabetes mellitus, cancer, the Wells scale, and CHA<sub>2</sub>DS<sub>2</sub>-VASc, with specific associations identified. Elevated MACE risk was observed, with a CCI score exceeding 2.67 ± 1.31 (<i>p</i> < 0.001), CHA2DS2-VASc score of 4.62 ± 1.02 (<i>p</i> < 0.001), and an intermediate-risk Wells scale classification. Overall, the AdaBoost ML offers an alternative predictive approach to facilitate the early identification of MACE risk in the assessment of patients with AF.https://www.mdpi.com/2227-7080/12/2/13atrial fibrillationmajor adverse cardiovascular events (MACE)machine learningartificial intelligence |
| spellingShingle | Pedro Moltó-Balado Silvia Reverté-Villarroya Victor Alonso-Barberán Cinta Monclús-Arasa Maria Teresa Balado-Albiol Josep Clua-Queralt Josep-Lluis Clua-Espuny Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation Technologies atrial fibrillation major adverse cardiovascular events (MACE) machine learning artificial intelligence |
| title | Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation |
| title_full | Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation |
| title_fullStr | Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation |
| title_full_unstemmed | Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation |
| title_short | Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation |
| title_sort | machine learning approaches to predict major adverse cardiovascular events in atrial fibrillation |
| topic | atrial fibrillation major adverse cardiovascular events (MACE) machine learning artificial intelligence |
| url | https://www.mdpi.com/2227-7080/12/2/13 |
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