Evolving complex yet interpretable representations: application to Alzheimer’s diagnosis and prognosis

Abstract

With increasing accuracy and availability of more data, the potential of using machine learning (ML) methods in medical and clinical applications has gained considerable interest. However, the main hurdle in translational use of ML methods is the lack of explainability, especially when non-linear methods are used. Explainable (i.e. human-interpretable) methods can provide insights into disease mechanisms but can equally importantly promote clinician-patient trust, in turn helping wider social acceptance of ML methods. Here, we empirically test a method to engineer complex, yet interpretable, representations of base features via evolution of context-free grammar (CFG). We show that together with a simple ML algorithm evolved features provide higher accuracy on several benchmark datasets and then apply it to a real word problem of diagnosing Alzheimer’s disease (AD) based on magnetic resonance imaging (MRI) data. We further demonstrate high performance on a hold-out dataset for the prognosis of AD.