The Importance of Nonlinear Transformations Use in Medical Data Analysis.

ABSTRACT:

Background

The accumulation of data and its accessibility through easier-to-use platforms will allow data scientists and practitioners who are less sophisticated data analysts to get answers by using big data for many purposes in multiple ways. Data scientists working with medical data are aware of the importance of preprocessing, yet in many cases, the potential benefits of using nonlinear transformations is overlooked.

Objective

Our aim is to present a semi-automated approach of symmetry-aiming transformations tailored for medical data analysis and its advantages.

Methods

We describe 10 commonly encountered data types used in the medical field and the relevant transformations for each data type. Data from the Alzheimer's Disease Neuroimaging Initiative study, Parkin

SUBMITTER: Shachar N 

PROVIDER: S-EPMC5970282 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature