Name
Title | ||
|---|---|---|
The Forecast Of The Postoperative Survival Time Of Patients Suffered From Non-Small Cell Lung Cancer Based On Pca And Extreme Learning Machine |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1142/S0129065706000494 | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
non-small cell lung cancer, feedforward neural networks, extreme learning machine, principal component analysis | Lung cancer,Feedforward neural network,Survival rate,Computer science,Extreme learning machine,Rate of convergence,Artificial intelligence,Backpropagation,Machine learning,Principal component analysis | Journal |
Volume | Issue | ISSN |
16 | 1 | 0129-0657 |
Citations | PageRank | References |
7 | 0.60 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Han | 1 | 241 | 26.37 |
| De-Shuang Huang | 2 | 5532 | 357.50 |
| Zhi-Hua Zhu | 3 | 14 | 1.44 |
| Tie-Hua Rong | 4 | 7 | 0.60 |