Application of multiview techniques to NHANES dataset

TL;DR

This paper explores multiview learning with Canonical Correlation Analysis on NHANES data to enhance disease classification, demonstrating improved performance in diabetes prediction by leveraging multiple health data components.

Contribution

It introduces a multiview approach using CCA on NHANES data for disease classification, highlighting the benefit of integrating diverse health data views.

Findings

Multiview features improved diabetes classification accuracy.

CCA effectively captured correlations between different health data components.

The approach demonstrated potential for better disease prediction models.

Abstract

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.