J Appl Biomed 15:151-159, 2017 | DOI: 10.1016/j.jab.2016.12.001

Prediction of post-operative survival expectancy in thoracic lung cancer surgery with soft computing

Mohammad Saber Iraji

Faculty Member of Department of Computer Engineering and Information Technology, Payame Noor University, Iran

Prediction of survival expectancy after surgery is so important. Soft computing approaches using training data are good approximations to model the different systems.
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: Thoracic surgery; Lung cancer; Adaptive fuzzy neural network; ELM; Neural networks

Received: May 14, 2016; Accepted: December 21, 2016; Published: May 1, 2017  Show citation