GBoost: A novel Grading-AdaBoost ensemble approach for automatic identification of erythemato-squamous disease

Ensemble learning is one of the powerful machine learning approaches that is generally used to strengthen models by combining the performances of several weak learners. It holds a great potential for solving umpteen problems in healthcare domain by enabling health systems to use data analytically for identifying best practices that improves healthcare and additionally reduces the cost too. The main focus of the present work is the automatic identification of erythemato-squamous disease (ESD) with higher accuracy performance, thereby, an ESD prediction system has been proposed using ensemble approach. The present study introduces GBoost (GB) ensemble framework that is based on grading approach with AdaBoost scheme for analysis and prediction of erythemato-squamous disease (ESD). The experiments were performed using dermatology dataset. The ESD prediction system uses imputation and filter approaches for data preprocessing and includes two phases for building models. In the first phase, models have been built using individual classifiers without using any ensemble technique whereas the second phase includes the GB ensemble along with dynamic base-classifiers and static meta-classifier for model building. At the end, the best classifier from phase one (without using GB ensemble framework) has been compared with the best GB ensemble set (using GB ensemble framework) from phase 2 to obtain the overall best model for ESD prediction. The proposed ESD prediction system using GB ensemble framework has achieved an accuracy of 99.45% which is higher than all the previous works on this dataset. The use of ensemble learning in this study exhibits a remarkable performance in the automatic identification of Erythemato-sequamous disease (ESD) with augmented accuracy.