Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making

Clinical data sets have very special properties and suffer from many caveats in machine learning. They typically show a high-class imbalance, have a small number of samples and a large number of parameters, and have missing values. While feature selection approaches and imputation techniques address the former problems, the class imbalance is typically addressed using augmentation techniques. However, these techniques have been developed for big data analytics, and their suitability for clinical data sets is unclear. This study analyzed different augmentation techniques for use in clinical data sets and subsequent employment of machine learning-based classification. It turns out that Gaussian Noise Up-Sampling (GNUS) is not always but generally, is as good as SMOTE and ADASYN and even outperform those on some datasets. However, it has also been shown that augmentation does not improve classification at all in some cases.

Automated summary

Clinical data sets have unique properties and challenges in machine learning, with class imbalance, small sample sizes, and missing values being common issues. Augmentation techniques are often used to address the class imbalance, with Gaussian Noise Up-Sampling (GNUS) showing promising results compared to other techniques like SMOTE and ADASYN on certain datasets. However, augmentation does not always lead to improved classification performance in some cases.