# Application of multiview techniques to NHANES dataset

arXiv: https://arxiv.org/abs/1608.04783 · HTML: https://mlsanity.com/paper/1608.04783/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2016.

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

- [Fair Canonical Correlation Analysis](https://mlsanity.com/paper/2309.15809/) (2023)

## Abstract (arXiv)

Disease prediction or classification using health datasets involve using well-known predictors associated with the disease as features for the models. This study considers multiple data components of an individual's health, using the relationship between variables to generate features that may improve the performance of disease classification models. In order to capture information from different aspects of the data, this project uses a multiview learning approach, using Canonical Correlation Analysis (CCA), a technique that finds projections with maximum correlations between two data views. Data categories collected from the NHANES survey (1999-2014) are used as views to learn the multiview representations. The usefulness of the representations is demonstrated by applying them as features in a Diabetes classification task.