Hybrid(Penalized Regression and MLP) Models for Outcome Prediction in HDLSS Health Data

TL;DR

This paper introduces a hybrid modeling approach combining penalized regression and MLPs for improved diabetes outcome prediction in high-dimensional health data, demonstrating enhanced accuracy over traditional models.

Contribution

It presents a novel hybrid model that integrates XGBoost feature encoding with a lightweight MLP, improving predictive performance in HDLSS health datasets.

Findings

Hybrid model outperforms baseline models in AUC and accuracy

Code and scripts are publicly released for reproducibility

Demonstrates effectiveness in high-dimensional health data

Abstract

I present an application of established machine learning techniques to NHANES health survey data for predicting diabetes status. I compare baseline models (logistic regression, random forest, XGBoost) with a hybrid approach that uses an XGBoost feature encoder and a lightweight multilayer perceptron (MLP) head. Experiments show the hybrid model attains improved AUC and balanced accuracy compared to baselines on the processed NHANES subset. I release code and reproducible scripts to encourage replication.