phat 21(25): e2

Research Article

Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm

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  • @ARTICLE{10.4108/eai.8-1-2021.167845,
        author={Prem Kumar Chandrakar and Akhilesh Kumar Shrivas and Neelam Sahu},
        title={Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={7},
        number={25},
        publisher={EAI},
        journal_a={PHAT},
        year={2021},
        month={1},
        keywords={Gene Expression, Modified Genetic Algorithm (MGA), Ensemble, Proposed Ensemble Model (PEM), Microarray, Lung Cancer},
        doi={10.4108/eai.8-1-2021.167845}
    }
    
  • Prem Kumar Chandrakar
    Akhilesh Kumar Shrivas
    Neelam Sahu
    Year: 2021
    Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm
    PHAT
    EAI
    DOI: 10.4108/eai.8-1-2021.167845
Prem Kumar Chandrakar1,*, Akhilesh Kumar Shrivas2, Neelam Sahu3
  • 1: Department of Computer Science, Mahant Laxminarayan Das College, Raipur (C.G.) India
  • 2: Department of Computer Science and Information Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur. India
  • 3: Department of Information Technology and Computer Science, Dr. C.V. Raman University, Kota, Bilaspur. India
*Contact email: prem.k.chandrakar@gmail.com

Abstract

INTRODUCTION: Gene expression levels are important for identifying and diagnosing diseases like cancer. Gene expression microarray information contains a high extent feature set, which minimizes the performance and the accuracy of classifiers.

OBJECTIVES: This paper proposes a Modified Genetic Algorithm (MGA) that is based on Classifier Subset Evaluators – Genetic Search (Eval-CSE_GS) for selecting the relevant feature subsets. The MGA feature selection procedure is applied to microarray information for cancer patients that minimize a high dimension feature subset into low dimension feature subsets.

METHODS: The various data mining methods for classifying the various kinds of cancer disease patients are presented. The proposed model refers to an ensemble model (PEM) for the organization of cancer disease by reducing the feature subsets, which results show improvements in the success rate.

RESULTS: The proposed ensemble model obtains the accuracy of 94.58%, 96.56% and 97.04% for PEM-1 to PEM-3, respectively.

CONCLUSION: Our proposed MGA-PEM model gives satisfactory results for cancer identification and classification.