Machine learning-based lung cancer diagnosis

Cancer is one of the leading health problems, occurring in various organs and tissues body, its incidence increasing worldwide. Lung cancer deadliest types cancer. Due to worldwide prevalence, number cases, deadly consequences, early detection lung cancer, as with all other cancers, greatly increases chances survival. As diseases, diagnosis only possible after appearance symptoms an examination by specialists. Known are shortness breath, coughing, wheezing, jaundice fingers, chest pain, difficulty swallowing. The made expert on site based these additional tests. aim this study detect disease at earlier stage present, assess more cases less time cost, achieve results new situations that successful or even faster than those human experts deriving them from existing data using different algorithms. develop automated model can early-stage machine learning methods. developed includes nine algorithms (NB, LR, DT, RF, GB, SVM). success classification used was evaluated metrics accuracy, sensitivity, precision calculated parameters confusion matrix. obtained show proposed a maximum accuracy 91%.