Predicting lane-change intents is crucial for driving automation. Several rule-based models and data-based algorithms exist in the literature, amongs other modelling approaches. In this contribution, we compare lane-change intent prediction using MOBIL rule-based model and a decision tree algorithm. Both approaches are based on the speed difference and spacing with the four surrounding vehicles on current and intended lanes. The data are collected from the highway Drone (HighD) trajectory data-set of two-lane German highways.We extract from the trajectories lane-keeping and lane-changing maneuvers, including lane-keeping on right and left lane and lane-changing to the right and the left lane. The behavior of the driver significantly changes according to the maneuver. It turns out that changing the lane to the right (fold-down) is a more complex process than lane-changing to the left (overtaking). Indeed, overtaking results from a mechanism with the neighbors mainly based on three parameters, while folddown requires more complex combinations. This leads to different meanings of the spacing variables with the neighboring vehicles and requires analysing the maneuvers separately. We compute and compare the prediction errors of lane-changing and lane-keeping intents for the rule-based MOBIL and decision tree approaches. The databased algorithm, devoid of modeling bias, can predict both overtaking and fold-down maneuvers accurately.