eHealth services category has a diversified set of traffic patterns and demands in terms of QoS assurances. Existing QoS solutions were designed to support only aggregated classes of service and cannot differentiate traffic based on an application's behavioral pattern. In order to improve the performance of eHealth applications for home and mobile users there is a need to develop new traffic identification techniques, which would work at the edge of the network. This paper addresses the above problem by proposing machine learning-based approach for eHealth traffic identification. We investigate different techniques which combine the results from multiple machine learning classifiers and show which combination of techniques is best suited for identifying diverse eHealth traffic. Our approach is validated in a mobile e-health application context and the results prove that multi-classification techniques can be used in practice to provide application-based service differentiation.