%0 Journal Article
%T Predicting Lung Cancer Stage by Expressions of Protein-Encoding Genes
%A Sicong Chen
%J Advances in Bioscience and Biotechnology
%P 368-377
%@ 2156-8502
%D 2023
%I Scientific Research Publishing
%R 10.4236/abb.2023.148024
%X Predicting the stages of cancer
accurately is crucial for effective treatmen</span><span style=\"font-family:&quot;\">t planning. In this study, we aimed to develop a model using gene
expression d<span>ata and XGBoost (eXtreme
Gradient Boosting) that include clinical an</span>d dem<span>ographic variables to predict specific lung cancer stages in patients. B</span>y
conducting the feature selection using the Wilcoxon Rank Test, we picked the
most impactful genes associated with lung cancer stage prediction. Our model
achieved an overall accuracy of 82% in classifying lung cancer stages acco<span>rding to patients¡¯ gene expression data. These
findings demonstrate </span>the potential of gene expression analysis and
machine learning techniques in improving the accuracy of lung cancer stage
prediction, aiding in personalized treatment decisions.
%K Lung Cancer Prediction
%K XGBoost
%K Central Dogma
%K Feature Selection
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=127231