Interpretable Prediction of Diabetes from Tabular Health Screening Records Using an Attentional Neural Network

Abstract

Health screening is conducted in numerous countries to observe general health conditions. Machine learning has been applied to health screening records to predict asymptomatic patients' future medical states. However, for medical researchers and physicians, it is crucial to know why machine learning methods made such predictions to understand the underlying mechanism of the disease and prescribe treatments; therefore, predictions must be interpretable. We investigated the ability of an attentional neural network that processes tabular data, namely TabNet, to determine attributes that contribute to making predictions of the aggravation of type 2 diabetes. We used both model-agnostic and model-specific interpretation methods. For the former, we tested SHapley Additive exPlanations (SHAP). For the latter, we used model-specific feature importance and the mask in the attentive transformer of TabNet. We found that this mask provides useful information regarding which items in a biochemical analysis affect the aggravation of type 2 diabetes. The results from model-agnostic and model-specific methods were consistent.