Hybrid(Penalized Regression and MLP) Models for Outcome Prediction in HDLSS Health Data
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866917118605262848 |
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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 |