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.
Keywords: 1D CNN; 1D convolutional neural network; ECG analysis; cardiovascular risk; childhood obesity; neural architecture search.