Comparative Analysis of MDL-VAE vs. Standard VAE on 202 Years of Gynecological Data

TL;DR

This paper compares MDL-VAE and standard VAE on 202 years of gynecological data, showing MDL-VAE's superior reconstruction accuracy, structured latent space, and robustness, highlighting its potential for healthcare data analysis.

Contribution

It introduces a comparative evaluation demonstrating that MDL-VAE outperforms standard VAE in reconstructing complex healthcare data with better regularization and latent structure.

Findings

MDL-VAE achieves significantly lower reconstruction errors.

MDL-VAE produces more structured and meaningful latent representations.

The approach demonstrates robustness and efficiency in healthcare data modeling.

Abstract

This study presents a comparative evaluation of a Variational Autoencoder (VAE) enhanced with Minimum Description Length (MDL) regularization against a Standard Autoencoder for reconstructing high-dimensional gynecological data. The MDL-VAE exhibits significantly lower reconstruction errors (MSE, MAE, RMSE) and more structured latent representations, driven by effective KL divergence regularization. Statistical analyses confirm these performance improvements are significant. Furthermore, the MDL-VAE shows consistent training and validation losses and achieves efficient inference times, underscoring its robustness and practical viability. Our findings suggest that incorporating MDL principles into VAE architectures can substantially improve data reconstruction and generalization, making it a promising approach for advanced applications in healthcare data modeling and analysis.