Ann Transl Med 2021 Apr;9(8):623
Department of Thoracic Surgery, The Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing, China.
Background: Lung cancer is the most threatening malignant tumor to human health and life. Using a variety of machine learning algorithms and statistical analyses, this paper explores, discovers and demonstrates new indicators for the early diagnosis of lung cancer and their diagnostic performance from large samples of clinical data in the real world.
Methods: By applying machine learning methods, including minimum description length (MDL), naive Bayesian (NB), K-means (KM), nonnegative matrix factorization (NMF), and decision tree (DT), based on large sample data of 2,502 patients, we built a classification model and systematically explored differences in fibrinogen levels in different clinical stages of lung cancer between the sexes. Read More