The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis
The increasing prevalence of overweight and obesity is a worldwide problem, with several well-known consequences that might start to develop early in life during childhood. The present research based on data from children that have been followed since birth in a previously established cohort study (...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Children |
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| Online Access: | https://www.mdpi.com/2227-9067/10/10/1655 |
| _version_ | 1827721345109065728 |
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| author | João Rala Cordeiro Sara Mosca Ana Correia-Costa Cátia Ferreira Joana Pimenta Liane Correia-Costa Henrique Barros Octavian Postolache |
| author_facet | João Rala Cordeiro Sara Mosca Ana Correia-Costa Cátia Ferreira Joana Pimenta Liane Correia-Costa Henrique Barros Octavian Postolache |
| author_sort | João Rala Cordeiro |
| collection | DOAJ |
| description | The increasing prevalence of overweight and obesity is a worldwide problem, with several well-known consequences that might start to develop early in life during childhood. The present research based on data from children that have been followed since birth in a previously established cohort study (Generation XXI, Porto, Portugal), taking advantage of State-of-the-Art (SoA) data science techniques and methods, including Neural Architecture Search (NAS), explainable Artificial Intelligence (XAI), and Deep Learning (DL), aimed to explore the hidden value of data, namely on electrocardiogram (ECG) records performed during follow-up visits. The combination of these techniques allowed us to clarify subtle cardiovascular changes already present at 10 years of age, which are evident from ECG analysis and probably induced by the presence of obesity. The proposed novel combination of new methodologies and techniques is discussed, as well as their applicability in other health domains. |
| first_indexed | 2024-03-10T21:20:16Z |
| format | Article |
| id | doaj.art-0742e569456a4a8da65a695f4305b41d |
| institution | Directory Open Access Journal |
| issn | 2227-9067 |
| language | English |
| last_indexed | 2024-03-10T21:20:16Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Children |
| spelling | doaj.art-0742e569456a4a8da65a695f4305b41d2023-11-19T16:05:11ZengMDPI AGChildren2227-90672023-10-011010165510.3390/children10101655The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science AnalysisJoão Rala Cordeiro0Sara Mosca1Ana Correia-Costa2Cátia Ferreira3Joana Pimenta4Liane Correia-Costa5Henrique Barros6Octavian Postolache7Instituto de Telecomunicações, IT-IUL, Iscte—Instituto Universitário de Lisboa, 1649-026 Lisbon, PortugalPediatric Nephrology Unit, Centro Materno-Infantil do Norte, Centro Hospitalar Universitário de Santo António, 4099-001 Porto, PortugalDivision of Paediatric Cardiology, Centro Hospitalar Universitário São João, 4200-319 Porto, PortugalEPIUnit—Instituto de Saúde Pública, Universidade do Porto, Rua das Taipas, n° 135, 4050-600 Porto, PortugalDivision of Paediatric Cardiology, Centro Hospitalar Universitário São João, 4200-319 Porto, PortugalPediatric Nephrology Unit, Centro Materno-Infantil do Norte, Centro Hospitalar Universitário de Santo António, 4099-001 Porto, PortugalEPIUnit—Instituto de Saúde Pública, Universidade do Porto, Rua das Taipas, n° 135, 4050-600 Porto, PortugalInstituto de Telecomunicações, IT-IUL, Iscte—Instituto Universitário de Lisboa, 1649-026 Lisbon, PortugalThe increasing prevalence of overweight and obesity is a worldwide problem, with several well-known consequences that might start to develop early in life during childhood. The present research based on data from children that have been followed since birth in a previously established cohort study (Generation XXI, Porto, Portugal), taking advantage of State-of-the-Art (SoA) data science techniques and methods, including Neural Architecture Search (NAS), explainable Artificial Intelligence (XAI), and Deep Learning (DL), aimed to explore the hidden value of data, namely on electrocardiogram (ECG) records performed during follow-up visits. The combination of these techniques allowed us to clarify subtle cardiovascular changes already present at 10 years of age, which are evident from ECG analysis and probably induced by the presence of obesity. The proposed novel combination of new methodologies and techniques is discussed, as well as their applicability in other health domains.https://www.mdpi.com/2227-9067/10/10/1655cardiovascular riskchildhood obesityECG analysisneural architecture search1D convolutional neural network1D CNN |
| spellingShingle | João Rala Cordeiro Sara Mosca Ana Correia-Costa Cátia Ferreira Joana Pimenta Liane Correia-Costa Henrique Barros Octavian Postolache The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis Children cardiovascular risk childhood obesity ECG analysis neural architecture search 1D convolutional neural network 1D CNN |
| title | The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis |
| title_full | The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis |
| title_fullStr | The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis |
| title_full_unstemmed | The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis |
| title_short | The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis |
| title_sort | association between childhood obesity and cardiovascular changes in 10 years using special data science analysis |
| topic | cardiovascular risk childhood obesity ECG analysis neural architecture search 1D convolutional neural network 1D CNN |
| url | https://www.mdpi.com/2227-9067/10/10/1655 |
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