Improved Artificial Neural Network with State Order Dataset Estimation for Brain Cancer Cell Diagnosis. (16th April 2022)

• Record Type: Journal Article
• Authors: Indira, D. N. V. S. L. S.; Ganiya, Rajendra Kumar; Ashok Babu, P.; Xavier, A. Jasmine; Kavisankar, L.; Hemalatha, S.; Senthilkumar, V.; Kavitha, T.; Rajaram, A.; Annam, Karthik; Yeshitla, Alazar
• Other Names: Teekaraman Yuvaraja Academic Editor.
• Is Part Of: BioMed research international. Volume 2022(2022)
• Publication Date: 2022-04-16
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2022/7799812
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : 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%, F -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).