Ensemble Learning-Based Pain Intensity Identification Model Using Facial Expressions

The pain identification model (PIM) can assist the healthcare professionals to render effective services to individuals. Disabled individuals can benefit from the automated PIM. Ensemble learning is widely employed for developing medical applications. A model for classifying the pain intensity using facial expression images is proposed in this study. A ShuffleNet V2 model is fine-tuned to extract features using fusion feature and class activation map techniques. CatBoost and XGBoost models are used as base models to predict pain intensities. The authors used the support vector machine (SVM) model as a meta-model to produce a final outcome. They optimize the SVM model in order to identify pain using the predictions of the base models. The model is generalized using the University of Northern British Columbia–McMaster dataset. The dataset encompasses 200 videos and 48,000 annotated images. The comparative analysis outcome highlights the exceptional performance of the proposed PIM. An optimal accuracy of 98.7% and an F1-score of 98.0% indicate the effectiveness of the proposed model. The uncertainty analysis outcome revealed that the model is reliable and can be deployed in healthcare centers. However, substantial training is required to boost the efficiency of the proposed model in real-time settings.