Predicting the stages of cancer
accurately is crucial for effective treatment planning. In this study, we aimed to develop a model using gene
expression data and XGBoost (eXtreme
Gradient Boosting) that include clinical and demographic variables to predict specific lung cancer stages in patients. By
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 according to patients’ gene expression data. These
findings demonstrate the potential of gene expression analysis and
machine learning techniques in improving the accuracy of lung cancer stage
prediction, aiding in personalized treatment decisions.
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