The Importance of Nonlinear Transformations Use in Medical Data Analysis

JMIR Medical Informatics
Netta ShacharYoav Benjamini

Abstract

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. Our aim is to present a semi-automated approach of symmetry-aiming transformations tailored for medical data analysis and its advantages. 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, Parkinson's disease hospital cohort, and disease-simulating data were used to demonstrate the approach and its benefits. Symmetry-targeted monotone transformations were applied, and the advantages gained in variance, stability, linearity, and clustering are demonstrated. An open source application implementing the described methods was developed. Both linearity of relationships and increase of stability of variability improved after applying proper nonlinear transformation. Clustering simulated nons...Continue Reading

References

May 1, 1989·Statistics in Medicine·S Durrleman, R Simon
Aug 13, 2013·Alzheimer's & Dementia : the Journal of the Alzheimer's Association·Michael W WeinerUNKNOWN Alzheimer's Disease Neuroimaging Initiative
Jan 21, 2015·JMIR Medical Informatics·Weiqi Wang, Eswar Krishnan

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