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A computational analysis to predict BMI via intelligent techniques
Keywords
Computational analysis, BMI, Body Mass Index, Intelligent techniques
Abstract
The Body Mass Index (BMI) is widely recognized as a key indicator of an individual’s overall health. This contribution presents a computational approach to predict BMI using both traditional statistical methods and Artificial Intelligence techniques, namely Linear Regression, Random Forest, Decision Trees, and Neural Networks. The dataset is composed of health metrics from a sample of women in Belgium collected between 2000 and 2001. This contribution compares the predictive performance of the afore mentioned models and discusses the significance of different health-related indicators on BMI. The results demonstrate the effectiveness of both linear and AI-based approaches in predicting BMI, and provide valuable insights for researchers and practitioners interested in the quantitative assessment of health metrics and disease prediction.