Early Lung Cancer Prediction approach based on Gene Disorder using Improved GA and Decision Tree approach

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.