The Promise of Machine Learning: When Will it be Delivered?

ABSTRACT

BACKGROUND: The real-life applications of machine learning clinical decision making is currently lagging behind its promise. One of the critics on machine learning is that it doesn't outperform more traditional statistical approaches in every problem. METHODS AND RESULTS: Authors of "Predictive Abilities of Machine Learning Techniques May Be Limited by Dataset Characteristics Insights From the UNOS Database" presented in the current issue of the Journal of Cardiac Failure that machine learning approaches do not provide significantly higher performance when compared to more traditional statistical approaches in predicting mortality following heart transplant. In this brief report, we provide an insight on the possible reasons for why machine learning methods do not outperform more traditional approaches for every problem and every dataset. CONCLUSIONS: Most of the performance-focused critics on machine learning are because the bar is set unfairly too high for machine learning. In most cases, machine learning methods provides at least as good results as traditional statistical methods do. It is normal for machine learning models to provide similar performance with linear models if the actual underlying input-outcome relationship is linear. Moreover, machine learning methods outperforms linear statistical models when the underlying input-output relationship is not linear and if the dataset is large enough and include predictors capturing that nonlinear relationship.