Lung cancer typology classification based on biochemical markers using machine learning techniques
Clinical mistreatment and mismanagement are big issues caused by detection of too many false negative patients. Therefore, lung cancer diagnostic inaccuracy and methods to surpass it in a minimally invasive way is often the subject of research, as it is case of this study. This study focuses on the use of machine learning algorithms as a noninvasive tool to differentiate malignant pleural effusions from benign effusions. It provides performance comparisons between Adaptive neuro-fuzzy inference system (ANFIS), Support vector machine (SVM), RUS Boosted Tree (RUSBoost) and K-Nearest-Neighbor (K-NN) techniques for lung cancer detection. The proposed algorithms were chosen based on the current state of the art in the field of pulmonary diagnostics. The novelty of this work is the application of machine learning models for classification of lung cancer based on expression of tumor markers obtained from serum and pleural fluids. The performance of all models is compared and validated on data samples of 168 patients. Three classification model, SVM, RUSBoost and K-NN performed equally well, whereas underperforming model was ANFIS.