Hybrid(Penalized Regression and MLP) Models for Outcome Prediction in HDLSS Health Data
This is an incremental improvement for health data analysis, applying existing methods to a specific dataset.
The paper tackled predicting diabetes status from NHANES health data by comparing baseline models with a hybrid XGBoost-MLP approach, resulting in improved AUC and balanced accuracy.
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.