Prediction of post-operative survival expectancy in thoracic lung cancer surgery with soft computing - Iraji, Mohammad Saber (Journal of Applied Biomedicine, 2017)
We present many solutions to predict 1-year the post-operative survival expectancy in thoracic lung cancer surgery base on artificial intelligence. We implement multi-layer architecture of SUB- Adaptive neuro fuzzy inference system (MLA-ANFIS) approach with various combinations of multiple input features, neural networks, regression and ELM (extreme learning machine) based on the used thoracic surgery data set with sixteen input features. Our results contribute to the ELM (wave kernel) based on 16 features is more accurate than different proposed methods for predict the post-operative survival expectancy in thoracic lung cancer surgery purpose.
Keywords: Survival Analysis; proporcionální rizikové modely; fuzzy logika; analýza přežití; pooperační období; Postoperative Period; Proportional Hazards Models; teoretické modely; Fuzzy Logic; Models, Theoretical; lidé; nádory plic; statistické modely; Humans; Models, Statistical; Lung Neoplasms