Numerical methods to predict health-related Metrics. A case of great economic impact: the Body Mass Index

Public health represents nowadays one of the major global challenges, especially with regards to the predictive analysis of biometric data. In this framework, Body Mass Index (referred to as BMI) represents one of the most recognized indicators for monitoring, understanding and forecasting the general health of the population. An analysis of BMI could have important social and economic impacts, allowing policy-makers to foster more and more sustainable economic development and to develop preventive strategies (and effective health policies) aimed at promoting social well-being. This contribution proposes the use of computational approaches to model BMI using both traditional statistical methods and AI techniques, namely linear regression, random forest, decision trees and neural networks. In particular, a comparative analysis of the predictive performance of the models mentioned above is proposed by discussing the significance of different health-related indicators on BMI. The computational analysis is conducted on a dataset consisting of health parameters of a sample of women in Belgium collected between 2000 and 2001. The results demonstrate the effectiveness of linear and AI-based approaches in predicting BMI and provide valuable information for policy makers interested in quantitative assessment of health parameters and dis- ease prediction to promote strategic choices that can contribute to improving collective well-being and reducing health and economic disparities, generating benefits at both the individual and community levels.