Decoding Health Trends: Exploring BMI Data with Deep Learning Ensemble Models using Stacked Autoencoders for Predictive Analysis

Abstract

This study delves into the exploration of health trends using advanced deep learning ensemble models applied to Body Mass Index (BMI) data. Specifically, we investigate the effectiveness of stacked autoencoders for predictive analysis, aiming to uncover intricate patterns underlying BMI fluctuations. The primary objective of this research is to discern nuanced insights into health trends, thus enabling informed decision-making and intervention strategies. The research employs a comprehensive dataset comprising BMI measurements and associated health parameters. Stacked autoencoders, a sophisticated deep learning architecture, serve as the primary tool for feature extraction and dimensionality reduction. By leveraging this technique, we construct a hierarchical representation of the data, capturing latent features contributing significantly to BMI variations. The ensemble framework integrates multiple autoencoder models, thereby enhancing predictive robustness and generalization performance. Our analysis yields compelling findings concerning BMI trends and their associations with various health indicators. The ensemble of stacked autoencoders demonstrates superior predictive performance, accurately capturing complex relationships within the data. Additionally, the model unveils subtle patterns and hidden factors influencing BMI fluctuations, providing valuable insights into underlying health dynamics. In conclusion, this study highlights the effectiveness of deep learning ensemble models, particularly stacked autoencoders, in unravelling health trends from BMI data. Through the application of these advanced analytical techniques, we gain deeper insights into the complexities of human health, paving the way for more effective strategies in health monitoring and intervention. The findings presented herein carry significant implications for healthcare practitioners, policymakers, and researchers aiming to address the challenges posed by evolving health trends and promote overall well-being in populations