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The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis.

João Rala Cordeiro1, Sara Mosca2, Ana Correia-Costa3

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Childhood obesity is linked to early heart changes, detectable through electrocardiogram (ECG) analysis. Advanced data science methods identified these subtle cardiovascular effects in 10-year-olds, highlighting potential health risks.

Keywords:
1D CNN1D convolutional neural networkECG analysiscardiovascular riskchildhood obesityneural architecture search

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Area of Science:

  • Cardiology
  • Pediatrics
  • Data Science

Background:

  • Childhood obesity is a growing global health concern with long-term consequences.
  • Cardiovascular changes can manifest early in life due to excess weight.
  • The Generation XXI cohort study in Porto, Portugal, provides longitudinal data on child development.

Purpose of the Study:

  • To investigate subtle cardiovascular changes in children associated with obesity.
  • To leverage advanced data science techniques for analyzing electrocardiogram (ECG) data.
  • To identify early indicators of obesity-related cardiovascular impact in children.

Main Methods:

  • Utilized State-of-the-Art (SoA) data science methods, including Neural Architecture Search (NAS).
  • Applied explainable Artificial Intelligence (XAI) and Deep Learning (DL) to analyze ECG records.
  • Employed a previously established birth cohort (Generation XXI) for data collection.

Main Results:

  • Identified subtle cardiovascular changes in 10-year-old children using ECG analysis.
  • These changes are likely induced by the presence of obesity.
  • Demonstrated the efficacy of combining NAS, XAI, and DL for uncovering hidden health data.

Conclusions:

  • Advanced data science techniques can reveal early cardiovascular alterations in obese children.
  • ECG analysis combined with AI offers a powerful tool for pediatric cardiovascular health monitoring.
  • The methodologies are potentially applicable to other health domains and data types.