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

Fuente: arXiv
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Autor principal: K, Mithra D
Formato: Preprint
Publicado: 2025
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author K, Mithra D
author_facet K, Mithra D
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2512_02489
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Hybrid(Penalized Regression and MLP) Models for Outcome Prediction in HDLSS Health Data
K, Mithra D
Machine Learning
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.
title Hybrid(Penalized Regression and MLP) Models for Outcome Prediction in HDLSS Health Data
topic Machine Learning
url https://arxiv.org/abs/2512.02489