Common Steps in Machine Learning Might Hinder The Explainability Aims in Medicine

Understand

Data pre-processing is a significant step in machine learning to improve the performance of the model and decreases the running time.

• This might include dealing with missing values, outliers detection and removing, data augmentation, dimensionality reduction, data normalization and handling the impact of confounding variables.
• Although it is found the steps improve the accuracy of the model, but they might hinder the explainability of the model if they are not carefully considered especially in medicine.
• They might block new findings when missing values and outliers removal are implemented inappropriately.