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.

集成学习是强大的机器学习方法之一，通常通过结合几个弱学习者的表现来增强模型。通过使卫生系统能够分析性地使用数据来确定可改善卫生保健并降低成本的最佳实践，它具有解决卫生保健领域众多问题的巨大潜力。本工作的主要重点是具有较高准确度性能的红细胞鳞状疾病（ESD）的自动识别，因此，已经提出了使用集成方法的ESD预测系统。本研究介绍了基于AdaBoost分级方法的GBoost（GB）集成框架，用于分析和预测红斑鳞状疾病（ESD）。使用皮肤病学数据集进行实验。ESD预测系统使用插补和滤波方法进行数据预处理，并包括用于构建模型的两个阶段。在第一阶段，使用单个分类器构建模型，而无需使用任何集成技术，而第二阶段包括GB集成以及用于模型构建的动态基础分类器和静态元分类器。最后，将第一阶段的最佳分类器（不使用GB集成框架）与第二阶段的最佳GB集合（使用GB集成框架）进行比较，以获得用于ESD预测的总体最佳模型。拟议的使用GB集成框架的ESD预测系统已达到99.45％的精度，高于该数据集上的所有先前工作。