Fig. 5: Feature interactions analysis for neural network models with small-scale clinical data (NG1 + PRS; NN and NG1; NN).

BMI body mass index, SBP systolic blood pressure, BP blood pressure, Hx history, NG non-genetic features, PRS polygenic risk scores, NG1 non-genetic feature set 1, NN model complexity subcategory 2 (neural network). a A heatmap of all 171 pairwise interaction weights between the 19 features in the NG1 + PRS; NN neural network model. The features with the greatest number and magnitude of interactions included age, BMI, and male sex. The PRSs were amongst the least-interacting features, but CAD_6M was the score most enriched for feature interactions. The mean interaction weight across all ten trials is shown. b A histogram describing the distribution of mean pairwise interaction weights (across ten trials) for each of the NG1 + PRS; NN models and its counterpart neural network model with just non-genetic data (NG1; NN). The inclusion of PRSs skews the distribution towards having smaller numbers of mean interaction weights in general. Interactions between PRSs and other features contribute to ~39% of all 171 pairwise interactions on the NG1 + PRS model. c A bar chart with the mean pairwise interaction weight and 95% confidence interval for each subcategory of feature interactions—NG–NG (two non-genetic features), NG–PRS (a non-genetic feature and a PRS), and PRS–PRS (two PRSs) for the NG1 + PRS; NN model. The NG–PRS and PRS–PRS pairs have significantly lower interaction weights than NG–NG pairs. d The pairwise interactions for the NG1 + PRS; NN model with the highest mean weights are shown. The top NG–PRS interactions include CAD_6M. e Scatterplot of NG–NG mean pairwise interaction weights between the NG1; NN and NG1 + PRS; NN models. Adding PRSs has a minimal effect on the distribution of NG–NG interaction weights.