Fig. 5: Application of proposed framework on systolic blood pressure (SBP) regression task using the Iwaki health promotion project (IHPP) dataset.

a Feature importance: these 25 features were selected by recursive feature elimination (RFE) to predict the SBP. RFE was performed with fivefold cross-validation, and the feature importance when 25 variables remained is shown for each fold (n = 5). The plot color represents the following: gray: variables which cannot be intervened, and blue: intervenable variables. Details of features are described in Supplementary Data 1. b Plot for prediction vs. true response variable. c Widely applicable Bayesian information criterion (WBIC) values of stochastic surrogate models with 1–8 mixture components. d, e Histogram of actionability scores with intervention variables based on data-driven selection (d) or hypothesis-driven selection (e) at different instances. An actionability score of zero indicates that the actionability of the optimal path is equivalent to that of the baseline path. f Comparison of predicted SBP reduction between framework-proposed paths and cardiologist-selected paths. Health improvement paths constructed based on hypothesis-driven intervention variables using our framework were compared with the cardiologist-selected paths among framework-proposed paths and random paths. Each cardiologist evaluated the same randomly selected instances (n = 10). Statistical significance was calculated using the Welch’s t-test (two-sided). In box-plot, center line represents median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range.