Cancer is one of the leading causes of death in the world. Cancer research is vital as the prognosis of cancer enables clinical applications for patients. In this study, we have proposed the Stacked Ensemble Model (Stacking of bagged and boosted learners) for the automatic disease diagnosis. The experimental results prove the superiority of the proposed method to conventional machine learning techniques. In the empirical study, the performance of eight data handling methods and 14 classification methods is compared to obtain prediction results. The performance of the model has been evaluated on five benchmark datasets. The appreciable Area under the Curve scores achieved by the proposed methodology on Cervical Cancer (0.98), Mesothelioma (0.93), Breast Cancer (0.99), Prostate Cancer (0.97), and Hepatitis-C Virus (0.998) datasets make this work more significant than the previously published works. The experimental results show that our proposed method is superior to conventional machine learning techniques and the proposed model contributes in the form of an efficient computational model.

中文翻译：

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基于不平衡数据的癌症诊断综合数据级调查

癌症是世界上主要的死亡原因之一。癌症研究至关重要，因为癌症的预后能够为患者提供临床应用。在这项研究中，我们提出了用于自动疾病诊断的 Stacked Ensemble Model（Stacking of bagged and boosted learners）。实验结果证明了该方法相对于传统机器学习技术的优越性。在实证研究中，比较了 8 种数据处理方法和 14 种分类方法的性能，以获得预测结果。该模型的性能已在五个基准数据集上进行了评估。所提出的方法在宫颈癌 (0.98)、间皮瘤 (0.93)、乳腺癌 (0.99)、前列腺癌 (0.97) 和丙型肝炎病毒 (0.97) 上获得的曲线下可观面积得分。998）数据集使这项工作比以前发表的作品更重要。实验结果表明，我们提出的方法优于传统的机器学习技术，并且提出的模型以高效计算模型的形式做出贡献。