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arXiv · 2512.02489

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

Mithra D K
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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.

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Mithra D K. 2025-12-02. Hybrid(Penalized Regression and MLP) Models for Outcome Prediction in HDLSS Health Data. https://arxiv.org/abs/2512.02489

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