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has been cited by the following article:

• TITLE: Predicting Lung Cancer Stage by Expressions of Protein-Encoding Genes

  AUTHORS: Sicong Chen

  KEYWORDS: Lung Cancer Prediction, XGBoost, Central Dogma, Feature Selection

  JOURNAL NAME: Advances in Bioscience and Biotechnology, Vol.14 No.8, August 25, 2023

  ABSTRACT: 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.