Longitudinal analyses of childhood obesity factors

Entity: Universitat Rovira i Virgili (URV)

Confidenciality: Si

Education area(s): Ciència de Dades Biomèdiques

APS: No

Title in different languages: Longitudinal analyses of childhood obesity factors

Abstract: This master thesis explores factors contributing to childhood obesity using data from hospitals in Catalonia, focusing on a cohort of pregnant women and their children up to age 5. Key parental and infant variables were analyzed to develop a predictive model for childhood obesity. Two new experimental methods were introduced: data transformation into increments and sequential pattern representation. A total of 150 patterns were identified and analyzed using Logistic Regression, Decision Tree, and Random Forest algorithms. The findings showed that these new methods were less effective compared to other state-of-the-art approaches.

Subject: Obesitat en els infants

Academic year: 2023-2024

Language: en

Work's public defense date: 2024-09-06

Subject areas: Computer engineering

Student: Práger, Zsófia

Department: Enginyeria Informàtica i Matemàtiques

Creation date in repository: 2025-03-14

Keywords: Sequential pattern mining, Machine learning, Childhood obesity

Title in original language: Longitudinal analyses of childhood obesity factors

Access Rights: info:eu-repo/semantics/openAccess

Project director: López Ibáñez, Beatriz Maria