Fig. 3: Feature importance and interpretability of risk contributions across data modalities. | Communications Medicine

Fig. 3: Feature importance and interpretability of risk contributions across data modalities.

From: Greater value add from electronic health records than polygenic risk scores for predicting myocardial infarction in machine learning

Fig. 3

PRS polygenic risk score features, Abs absolute value, LR logistic regression, NN feedforward neural network, NG1 non-genetic dataset 1 (known disease-specific risk factors), NG2 non-genetic dataset 2 (large-scale medical history embedding + known disease-specific risk factors), SBP systolic blood pressure, BMI body mass index, Hx history, Sig. significant. Diagnoses refers to diagnostic features in the NG2 dataset, excluding NG1 features. a Boxplots with the distribution of feature importance weights for the different data modalities in NG2 + PRS; LR model. Medical history (i.e., diagnostic) features have significantly lower weights on average compared to established risk factors and PRSs. b A scatterplot comparing the absolute value of the mean logistic regression feature importance weight for each of the 589 features in the NG2 + PRS; LR model to the p value of each feature. The p values were calculated using a one-sample two-tailed t-test for each feature weight across ten trials against a null hypothesis of zero weight. Significant features were selected after correcting for multiple comparisons based on the total number of features in each model using the Bonferroni correction (alpha = 0.01). Diagnostic feature 235 has the lowest p value and the highest absolute value mean feature weight. c The number of significant features for each data modality for the NG2 + PRS; LR model. The greatest number of features with importance weights significantly different from zero is from the medical history (i.e., diagnostic) feature set, but these 24 significant features made up <5% of all diagnostic features. In contrast, ~80% of the general datafield (i.e., established risk factor) features were significant. d (Left) The Pearson correlation in mean NG2 feature importance weights between NG2 + PRS; LR and NG2; LR models (i.e., with vs. without adding PRSs), (Right) The Pearson correlation in mean NG1 and PRS feature importance weights between NG1 + PRS; LR and NG2 + PRS; LR models. e Boxplots of positive and negative significant features in the NG2 + PRS; LR model, sorted by median importance weight across ten trials for each feature. Note: SBP listed as (1) and (2) represent the first and second of two consecutive measurements.

Back to article page