RESEARCH ARTICLE
Early Lung Cancer Prediction approach based on Gene Disorder using Improved GA and Decision Tree approach
Annamalai Anupriya1, Arunkumar Thangavelu2, *
Article Information
Identifiers and Pagination:
Year: 2023Volume: 17
E-location ID: e187412072303270
Publisher ID: e187412072303270
DOI: 10.2174/18741207-v17-e230419-2022-HT28-4371-6
Article History:
Received Date: 30/09/2022Revision Received Date: 27/12/2022
Acceptance Date: 03/02/2023
Electronic publication date: 08/05/2023
Collection year: 2023
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Abstract:
This research supports changes and variation of DNA sequence based on mutation of DNA gene arrangement over a gross chromosome irregularity. This change in gene disorder leads to new infectious diseases or abnormal changes in the human cellular body. This paper discusses the prediction of lung cancer traces, primarily due to mutations due to clinical and environmental factors exposure. The proposed model predicts the genetic phenotype from observed patients' relevant gene factors and non-genetic traces of lung cancer. Results and analysis show that the prediction rate supports an average of 73.81% of gene disorders compared with ACO and GA approaches.
Background:
The survey shows that most genetic diseases are the immediate consequence of a mutation in multiple genes. A survey and analysis of research work that supports changes and DNA variation of gene sequence, based on mutation DNA gene arrangement to a gross chromosome irregularity.
Objective:
This research aims to predict lung cancer cells based on genetic phenotype from its relevant gene factors and non-genetic traces of lung cancer from observed patient datasets.
Methods:
Major changes in gene disorder lead to abnormal changes in the human cellular body and hence the growth of cancerous tissues. The paper discusses the prediction of lung cancer traces, primarily due to gene mutations and exposure to climatic and environmental factors. An improved GA and decision tree approach as a classifier is designed and developed to support early prediction.
Results:
Analysis shows that the prediction accuracy rate supports an average of 73.81% of lung cancer based on gene disorder compared to the ACO and GA approaches.
Conclusion:
The result of the experiment shows that the approaches give more accuracy than the previous approaches.