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.
@article{arxiv.2512.02489,
title = {Hybrid(Penalized Regression and MLP) Models for Outcome Prediction in HDLSS Health Data},
author = {Mithra D K},
journal= {arXiv preprint arXiv:2512.02489},
year = {2025}
}