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

D N V S L S Indira1, Rajendra Kumar Ganiya2, P Ashok Babu3

  • 1Department of Information Technology, Seshadri Rao Gudlavalleru Engineering College, Gudlavalleru, Andra Pradesh, India.

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Summary

This study introduces an improved gene expression programming (IGEP) method for brain cancer cell analysis, achieving high accuracy in patient prognosis and classification. The IGEP approach significantly outperforms other machine learning models in identifying cancer subtypes.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning in Oncology

Background:

  • Brain cancer classification is complex due to cellular heterogeneity, impacting patient diagnosis and prognosis.
  • Accurate prognostication relies on detailed analysis of individual biocell appearance and gene expression patterns.
  • Existing artificial neural network (ANN) models show promise but require further enhancement for improved performance.

Purpose of the Study:

  • To develop and evaluate an advanced machine learning framework for precise brain cancer cell classification and patient prognosis.
  • To enhance feature selection and survival prediction using improved gene expression programming (IGEP) and principal component analysis (PCA).
  • To compare the proposed model's efficacy against established methods like GRNN, IELM, and SVM.

Main Methods:

  • Feature selection was performed using improved gene expression programming (IGEP) combined with a modified brute force algorithm.
  • Patient survival (maximum and minimum term) was classified using PCA integrated with an enhanced artificial neural network (EANN).
  • The Cancer Genome Atlas (CGA) dataset was utilized for system estimation and validation.

Main Results:

  • The proposed IGEP with modified brute force algorithm achieved high accuracy (96.37%), specificity (96.37%), and sensitivity (98.37%).
  • The model demonstrated strong performance with an F-measure of 80.22% and precision of 78.78%.
  • Simulation outputs confirmed superior performance of the IGEP-based approach compared to GRNN, IELM (with MRMR), and SVM.

Conclusions:

  • The IGEP method effectively selects crucial features, significantly improving brain cancer prognosis efficiency.
  • The developed system offers a robust and accurate tool for brain cancer subtype classification and survival prediction.
  • This research highlights the potential of advanced machine learning techniques in personalized oncology and cancer diagnostics.