# Using Machine Learning Techniques to Identify Key Risk Factors for Diabetes and Undiagnosed Diabetes

arXiv: https://arxiv.org/abs/2105.09379 · HTML: https://mlsanity.com/paper/2105.09379/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 64 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Gradient Boosting With Piece-Wise Linear Regression Trees](https://mlsanity.com/paper/1802.05640/) (2018)
- [An Ensemble Classifier for Predicting the Onset of Type II Diabetes](https://mlsanity.com/paper/1708.07480/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)

## Abstract (arXiv)

This paper reviews a wide selection of machine learning models built to predict both the presence of diabetes and the presence of undiagnosed diabetes using eight years of National Health and Nutrition Examination Survey (NHANES) data. Models are tuned and compared via their Brier Scores. The most relevant variables of the best performing models are then compared. A Support Vector Machine with a linear kernel performed best for predicting diabetes, returning a Brier score of 0.0654 and an AUROC of 0.9235 on the test set. An elastic net regression performed best for predicting undiagnosed diabetes with a Brier score of 0.0294 and an AUROC of 0.9439 on the test set. Similar features appear prominently in the models for both sets of models. Blood osmolality, family history, the prevalance of various compounds, and hypertension are key indicators for all diabetes risk. For undiagnosed diabetes in particular, there are ethnicity or genetic components which arise as strong correlates as well.