How to find combined feature contributions in a random forest algorithm
My recommendation would be to use Partial Dependence Plots, which show the marginal effect one or two features have on the predicted outcome.  They plot the average predictions over a range of values of the predictor(s) you specify, with the influence of all other predictors "averaged out".  If you are familiar with linear regression, it is like plotting the predicted value of the target for different values of a predictor multiplied by the estimated weight/coefficient.

In your question, you are interested in two predictors (smoking and family history), so you would plot a two-way partial dependence plot showing the dependence of the target variable on joint values of smoking and family history.

There is a PDPBox package you can install that produces a variety of very nice plots and has some tutorials: https://github.com/SauceCat/PDPbox.  It claims to support all Scikit-Learn algorithms.

There is also a Cross Validated Q&amp;A on interpreting PDP's for Random Forests (produced with R): https://stats.stackexchange.com/questions/121383/interpreting-y-axis-of-a-partial-dependence-plots