Department of Information Technology, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Andra Pradesh, India.
Department of Information Technology, Vignan's Institute of Information Technology, Visakhapatnam, Andra Pradesh, India.
Biomed Res Int. 2022 Apr 16;2022:7799812. doi: 10.1155/2022/7799812. eCollection 2022.
Brain cancer is one of the cell synthesis diseases. Brain cancer cells are analyzed for patient diagnosis. Due to this composite cell, the conceptual classifications differ from each and every brain cancer investigation. In the gene test, patient prognosis is identified based on individual biocell appearance. Classification of advanced artificial neural network subtypes attains improved performance compared to previous enhanced artificial neural network (EANN) biocell subtype investigation. In this research, the proposed features are selected based on improved gene expression programming (IGEP) with modified brute force algorithm. Then, the maximum and minimum term survivals are classified by using PCA with enhanced artificial neural network (EANN). In this, the improved gene expression programming (IGEP) effectual features are selected by using remainder performance to improve the prognosis efficiency. This system is estimated by using the Cancer Genome Atlas (CGA) dataset. Simulation outputs present improved gene expression programming (IGEP) with modified brute force algorithm which achieves accurate efficiency of 96.37%, specificity of 96.37%, sensitivity of 98.37%, precision of 78.78%, -measure of 80.22%, and recall of 64.29% when compared to generalized regression neural network (GRNN), improved extreme learning machine (IELM) with minimum redundancy maximum relevance (MRMR) method, and support vector machine (SVM).
脑癌是细胞合成疾病之一。对脑癌患者进行细胞分析。由于这种复合细胞，概念分类与每一项脑癌研究都不同。在基因测试中，根据个体生物细胞的外观来确定患者的预后。与之前的增强型人工神经网络 (EANN) 生物细胞亚型研究相比，先进的人工神经网络亚型的分类可获得更好的性能。在这项研究中，所提出的特征是基于改进的基因表达编程 (IGEP) 和改进的暴力算法选择的。然后，使用具有增强型人工神经网络 (EANN) 的 PCA 对最大和最小术语存活率进行分类。在这一点上，通过使用剩余性能来选择改进的基因表达编程 (IGEP) 有效的特征，以提高预测效率。该系统是使用癌症基因组图谱 (CGA) 数据集进行评估的。仿真输出显示，改进的基因表达编程 (IGEP) 和改进的暴力算法可实现 96.37%的准确率、96.37%的特异性、98.37%的灵敏度、78.78%的精度、80.22%的 -测量值和 64.29%的召回率，与广义回归神经网络 (GRNN)、具有最小冗余最大相关性 (MRMR) 方法的改进极限学习机 (IELM) 和支持向量机 (SVM) 相比。
