Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by using a graph-based recurrence classification machine learning framework that includes a graph neural network machine learning model and a recurrence classification machine learning model, where the recurrence classification machine learning model is configured to generate a predicted recurrence classification based at least in part on one or more graph-based features generated by a graph neural network machine learning model and one or more entity features associated with an entity identifier for an incoming event.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing entities, and/or the like, for providing a recommendation of a decision for an input case. According to some aspects, a method is provided. The method generally comprises receiving an input case data object; generating an input feature data object based at least in part on input case features of the input case data object; selecting a subgraph-based data structure from a graph-based data structure, the subgraph-based data structure identifying a subset of historical case data objects; generating a feature data model based at least in part on traversing the subgraph-based data structure; selecting a historical case cohort based at least in part on comparing the feature data model with the input feature data object; and providing a recommendation data object based at least in part on the historical case cohort.