-
Je něco špatně v tomto záznamu ?
Prediction of post-operative survival expectancy in thoracic lung cancer surgery with soft computing
Mohammad Saber Iraji
Jazyk angličtina Země Česko
- MeSH
- analýza přežití * MeSH
- fuzzy logika MeSH
- lidé MeSH
- nádory plic chirurgie MeSH
- pooperační období MeSH
- proporcionální rizikové modely MeSH
- statistické modely MeSH
- teoretické modely MeSH
- Check Tag
- lidé MeSH
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.
Literatura
- 000
- 00000naa a2200000 a 4500
- 001
- bmc17025097
- 003
- CZ-PrNML
- 005
- 20200518214139.0
- 007
- ta
- 008
- 170808s2017 xr ad f 000 0|eng||
- 009
- AR
- 024 7_
- $a 10.1016/j.jab.2016.12.001 $2 doi
- 040 __
- $a ABA008 $d ABA008 $e AACR2 $b cze
- 041 0_
- $a eng
- 044 __
- $a xr
- 100 1_
- $a Iraji, Mohammad Saber $u Faculty Member of Department of Computer Engineering and Information Technology, Payame Noor University, Iran
- 245 10
- $a Prediction of post-operative survival expectancy in thoracic lung cancer surgery with soft computing / $c Mohammad Saber Iraji
- 504 __
- $a Literatura
- 520 9_
- $a 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.
- 650 _2
- $a nádory plic $x chirurgie $7 D008175
- 650 _2
- $a pooperační období $7 D011184
- 650 12
- $a analýza přežití $7 D016019
- 650 _2
- $a proporcionální rizikové modely $7 D016016
- 650 _2
- $a statistické modely $7 D015233
- 650 _2
- $a lidé $7 D006801
- 650 _2
- $a fuzzy logika $7 D017143
- 650 _2
- $a teoretické modely $7 D008962
- 773 0_
- $t Journal of applied biomedicine $x 1214-021X $g Roč. 15, č. 2 (2017), s. 151-159 $w MED00012667
- 856 41
- $u https://jab.zsf.jcu.cz/pdfs/jab/2017/02/09.pdf $y plný text volně přístupný
- 910 __
- $a ABA008 $b B 2301 $c 1249 $y 4 $z 0
- 990 __
- $a 20170808075351 $b ABA008
- 991 __
- $a 20200518214153 $b ABA008
- 999 __
- $a ok $b bmc $g 1242136 $s 986017
- BAS __
- $a 3
- BMC __
- $a 2017 $b 15 $c 2 $d 151-159 $i 1214-021X $m Journal of Applied Biomedicine $x MED00012667
- LZP __
- $c NLK125 $d 20180107 $a NLK 2017-37/vt