Human addictive behavior prediction by using lime with ensemble model

Abstract

The data-driven techniques have utilized data mining and machine learning (ML) techniques in the biomedical and healthcare fields. The process of decision-making in uncertain contextual related to human addictions and emotions play an important role in the present research. The main aim of the research is to perform classification and generate a support system for uncertain addiction circumstances by proposing a technique for drug addiction treatment. The human behavior has majority shown challenges for the prediction of human behaviors that includes body poses estimation, movements and interaction with objects. This pose estimation has showed complexity with more pose aspects and the proposed research attempts to understand the human behaviors. The present research uses the local interpretable model-agnostic explanations (LIME) for finding the input features which are most important to generate a particular output based on decision service. LIME understands the model to perturb the data samples as an input and understands shows predictions change. Also, the ensemble classifier contains classifiers group that combines for performing the prediction of all unseen instances based on voting. The proposed LIME Feature-Ensemble classifier obtained 97.54% of accuracy when compared to the existing convolutional neural network (CNN) of 59.33% and Ensemble model of 93.33% accuracy.