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. 2022 Apr 16:2022:7799812.
doi: 10.1155/2022/7799812. eCollection 2022.

Improved Artificial Neural Network with State Order Dataset Estimation for Brain Cancer Cell Diagnosis

D N V S L S Indira  1 , Rajendra Kumar Ganiya  2 , P Ashok Babu  3 , A Jasmine Xavier  4 , L Kavisankar  5 , S Hemalatha  6 , V Senthilkumar  7 , T Kavitha  8 , A Rajaram  9 , Karthik Annam  3 , Alazar Yeshitla  10
Affiliations

Improved Artificial Neural Network with State Order Dataset Estimation for Brain Cancer Cell Diagnosis

D N V S L S Indira et al. Biomed Res Int. .

Retraction in

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).

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Conflict of interest statement

There is no conflict of interest.

Figures

Figure 1
Figure 1
Static order dataset predicting dynamic order brain cancer cell.
Figure 2
Figure 2
Confusion matrix for artificial binomial terms.
Figure 3
Figure 3
Enhanced artificial neural network.
Figure 4
Figure 4
Long- and short-time survivors class prediction by using the Kaplan-Meier curve of brain cancer prognosis prediction.
Figure 5
Figure 5
Area under the curve performance.
Figure 6
Figure 6
Accuracy performance comparison.
Figure 7
Figure 7
Sensitivity performance comparison.
Figure 8
Figure 8
Specificity performance comparison.
Figure 9
Figure 9
Precision performance comparison.
Figure 10
Figure 10
Recall performance comparison.
Figure 11
Figure 11
Overall performance prediction.
Algorithm 1
Algorithm 1
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