The Importance of Nonlinear Transformations Use in Medical Data Analysis.

Area of Science:

• Medical data science
• Big data analytics in healthcare
• Biostatistics

Background:

• Increasing accessibility of big data enables broader use in medical research.
• Standard data preprocessing often overlooks the benefits of nonlinear transformations for medical data.
• Nonlinear transformations can unlock deeper insights from complex medical datasets.

Purpose of the Study:

• To present a semi-automated approach for symmetry-aiming nonlinear transformations in medical data analysis.
• To highlight the advantages of these transformations for improving data quality and model robustness.
• To provide a practical tool for data scientists and medical practitioners.

Main Methods:

• Described transformations for 10 common medical data types.
• Applied symmetry-targeted monotone transformations to diverse datasets (Alzheimer's, Parkinson's, simulated data).
• Developed an open-source application to implement the described methods.

Main Results:

• Demonstrated improvements in variance, stability, linearity, and clustering after applying nonlinear transformations.
• Achieved significantly higher agreement (Rand value 0.986 vs. 0.681) in clustering simulated data by transforming to symmetry.
• Showcased enhanced linearity of relationships and increased stability of variability.

Conclusions:

• Semi-automated nonlinear transformations are crucial for enhancing medical data preprocessing.
• This approach enables simpler, more robust, and translational models, improving interpretability for medical practitioners.
• Integrating nonlinear transformations is essential for high-quality, interpretable medical data analysis.