Research Article
Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm
@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
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.
Copyright © 2021 Prem Kumar et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.