  Several decision points exist in business processes (e.g., whether a purchase
order needs a manager's approval or not), and different decisions are made for
different process instances based on their characteristics (e.g., a purchase
order higher than $500 needs a manager approval). Decision mining in process
mining aims to describe/predict the routing of a process instance at a decision
point of the process. By predicting the decision, one can take proactive
actions to improve the process. For instance, when a bottleneck is developing
in one of the possible decisions, one can predict the decision and bypass the
bottleneck. However, despite its huge potential for such operational support,
existing techniques for decision mining have focused largely on describing
decisions but not on predicting them, deploying decision trees to produce
logical expressions to explain the decision. In this work, we aim to enhance
the predictive capability of decision mining to enable proactive operational
support by deploying more advanced machine learning algorithms. Our proposed
approach provides explanations of the predicted decisions using SHAP values to
support the elicitation of proactive actions. We have implemented a Web
application to support the proposed approach and evaluated the approach using
the implementation.
