Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued only when the cancer is detected at the very early stage. Early detection of cancer is essential as, in the final stage, the chance of survival is limited. The symptoms of cancers are rigorous and therefore, all the symptoms should be studied properly before the diagnosis. Thus, an automatic prediction system is necessary for classifying cancer as malignant or benign. Hence, this paper introduces the novel strategy based on the JayaAnt lion optimization-based Deep recurrent neural network (JayaALO-based DeepRNN) for cancer classification. The steps followed in the developed model are data normalization, data transformation, feature dimension detection, and classification. The first step is data normalization. The goal of data normalization is to eliminate data redundancy and to mitigate the storage of objects in a relational database that maintains the same information in several places. After that, the data transformation is carried out based on log transformation that generates the patterns using more interpretable and helps fulfill the supposition, and to reduce skew. Also, the non-negative matrix factorization is employed for reducing the feature dimension. Finally, the proposed JayaALO-based DeepRNN method effectively classifies cancer based on the reduced dimension features to produce a satisfactory result. Thus, the resulted output of the proposed JayaALO-based DeepRNN is employed for cancer classification. The proposed JayaALO-based DeepRNN showed improved results with maximal accuracy of 95.97%, maximal sensitivity of 95.95%, and maximal specificity of 96.96%.

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中文翻译：

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Jaya Ant lion 优化驱动的深度循环神经网络使用基因表达数据进行癌症分类

癌症是世界范围内普遍存在的致命疾病之一，癌症患者只有在癌症早期被发现时才能获救。癌症的早期检测至关重要，因为在最后阶段，生存机会是有限的。癌症的症状是严格的，因此，在诊断之前应该对所有症状进行适当的研究。因此，自动预测系统对于将癌症分类为恶性或良性是必要的。因此，本文介绍了基于 JayaAnt 狮子优化的深度循环神经网络 (JayaALO-based DeepRNN) 的癌症分类新策略。在开发的模型中遵循的步骤是数据规范化、数据转换、特征维度检测和分类。第一步是数据规范化。数据规范化的目标是消除数据冗余并减少在多个位置维护相同信息的关系数据库中对象的存储。之后，基于日志转换进行数据转换，使用更可解释的模式生成模式，有助于实现假设，并减少偏差。此外，采用非负矩阵分解来减少特征维度。最后，提出的基于 JayaALO 的 DeepRNN 方法基于降维特征有效地对癌症进行分类，以产生令人满意的结果。因此，所提出的基于 JayaALO 的 DeepRNN 的结果输出用于癌症分类。提出的基于 JayaALO 的 DeepRNN 显示出改进的结果，最大准确度为 95.97%，最大灵敏度为 95.95%，

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