The article presents an experimental study on the effects of driving disengagement on takeover perormance in a simulated driving environment. Takeover performance was measured from participants (N=28, 14 females, age M=30.46, SD =10.67) as the response time (RT) required to complete the transition from automated to manual driving. Several other potential factors for takeover performance were also examined, including driver age, gender, simulator experience, driving-related data, and automotive user interface (UI) complexity (baseline vs. head-up display). A significant effect on RT was found for the type of disengagement (task vs. rest), as well as for the interaction effect of gender and disengagement. Males had significantly longer RT than females (difference in RT: M=2353.14 ms) when engaged in a secondary task. Machine learning was performed to examine the predictive performance of several regression models and the significance of the features (gender, age, driving disengagement, simulator experiance, average speed) on RT. The LightGBM regressor performed well (training accuracy: 0.89, test accuracy: .73, mean absolute error (MAE): .14). In addition to average speed and age, the disengagement features task, rest, and eyes-off-road ratio were the most important predictors of RT.