The forecast of the postoperative survival time of patients suffered from non-small cell lung cancer based on PCA and extreme learning machine.
In this paper, a new effective model is proposed to forecast how long the postoperative patients suffered from non-small cell lung cancer will survive. The new effective model which is based on the extreme learning machine (ELM) and principal component analysis (PCA) can forecast successfully the postoperative patients' survival time. The new model obtains better prediction accuracy and faster convergence rate which the model using backpropagation (BP) algorithm and the Levenberg-Marquardt (LM) algorithm to forecast the postoperative patients' survival time can not achieve. Finally, simulation results are given to verify the efficiency and effectiveness of our proposed new model.
Han
Fei
F
Intelligent Computation Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Science, Hefei, Anhui, 230031, China. hanfei1976@iim.ac.cn
Huang
De-Shuang
DS
Zhu
Zhi-Hua
ZH
Rong
Tie-Hua
TH
D000465:Algorithms; D002289:Carcinoma, Non-Small-Cell Lung / Q000401:mortality*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000401:mortality*; D008297:Male; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011184:Postoperative Period; D011237:Predictive Value of Tests; D025341:Principal Component Analysis; D012189:Retrospective Studies; D015996:Survival Rate