In the future, Air Traffic Controllers are expected to work together with more advanced computer-based automation that can automatically take action. The main challenge is then how to design computer-based tools such that they foster acceptance among air traffic controllers. One possible approach to foster acceptance is by matching the automated decisions and actions to individual human problem-solving styles, the so-called strategic conformance. Another approach is by making the automated tool more transparent and thus interpretable. Previous research aimed to combine these two approaches by making use of the Solution Space Diagram, a decision-support tool for Conflict Detection and Resolution, as a visual feature for a supervised machine learning method that aimed to generate individual human prediction models. Results were promising, but prediction accuracy could be significantly improved. In this study, the impact of feature engineering and a revised machine learning architecture on prediction accuracy will be investigated. This is done by evaluating different feature engineering and architecture options using data generated by a simulation in which Conflict Detection and Resolution is performed. It was found that a Convolutional Neural Network can accurately predict exact resolutions using regression and a more optimized architecture is introduced which significantly increases predictive performance. Furthermore, it is concluded that a larger solution space results in a slight increase in predictive performance whereas the use of a color scheme with more colors does not necessarily result in a higher predictive performance.