I am developing binary classification models to predict a medical condition in my dataset. My results show that both Logistic Regression and Linear SVM consistently outperformed other ML algorithms (SVM, NB, MLP and DT), as can be seen in the following screenshot:
Observing recent research, I found multiple studies and reviews that talk about the phenomenon of machine learning not being superior to logistic regression for clinical prediction models, such as this systematic review of 71 studies: https://pubmed.ncbi.nlm.nih.gov/30763612/.
I would like to understand what it means for LR to outperform other more complex ML algorithms? Does it just indicate the my classes are linearly separable?