Multimodal machine learning for analysing multifactorial causes of disease-The case of childhood overweight and obesity in Mexico.

ABSTRACT:

Background

Mexico has one of the highest global incidences of paediatric overweight and obesity. Public health interventions have shown only moderate success, possibly from relying on knowledge extracted using limited types of statistical data analysis methods.

Purpose

To explore if multimodal machine learning can enhance identifying predictive features from obesogenic environments and investigating complex disease or social patterns, using the Mexican National Health and Nutrition Survey.

Methods

We grouped features into five data modalities corresponding to paediatric population exogenous factors, in two multimodal machine learning pipelines, against a unimodal early fusion baseline. The supervised pipeline employed four methods: Linear classifier with Elastic Net r

SUBMITTER: Silva Sepulveda R 

PROVIDER: S-EPMC11752892 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature