Hybrid rough sets intelligent system architecture for survival analysis
Survival analysis challenges researchers because of two issues. First, in practice, the studies do not span wide enough to collect all survival times of each individual patient. All of these patients require censor variables and cannot be analyzed without special treatment. Second, analyzing risk fa...
Saved in:
Main Authors: | , , |
---|---|
Other Authors: | |
Format: | Conference or Workshop Item |
Published: |
2018
|
Subjects: | |
Online Access: | https://repository.li.mahidol.ac.th/handle/123456789/24390 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Mahidol University |
id |
th-mahidol.24390 |
---|---|
record_format |
dspace |
spelling |
th-mahidol.243902018-08-24T08:56:36Z Hybrid rough sets intelligent system architecture for survival analysis Puntip Pattaraintakorn Nick Cercone Kanlaya Naruedomkul King Mongkut's Institute of Technology Ladkrabang York University Mahidol University Computer Science Mathematics Survival analysis challenges researchers because of two issues. First, in practice, the studies do not span wide enough to collect all survival times of each individual patient. All of these patients require censor variables and cannot be analyzed without special treatment. Second, analyzing risk factors to indicate the significance of the effect on survival time is necessary. Hence, we propose "Enhanced Hybrid Rough Sets Intelligent System Architecture for Survival Analysis" (Enhanced HYRIS) that can circumvent these two extra issues. Given the survival data set, Enhanced HYRIS can analyze and construct a life time table and Kaplan-Meier survival curves that account for censor variables. We employ three statistical hypothesis tests and use the p-value to identify the significance of a particular risk factor. Subsequently, rough set theory generates the probe reducts and reducts. Probe reducts and reducts include only a risk factor subset that is large enough to include all of the essential information and small enough for our survival prediction model to be created. Furthermore, in the rule induction stage we offer survival prediction models in the form of decision rules and association rules. In the validation stage, we provide cross validation with ELEM2 as well as decision tree. To demonstrate the utility of our methods, we apply Enhanced HYRIS to various data sets: geriatric, melanoma and primary biliary cirrhosis (PBC) data sets. Our experiments cover analyzing risk factors, performing hypothesis tests and we induce survival prediction models that can predict survival time efficiently and accurately. © Springer-Verlag Berlin Heidelberg 2007. 2018-08-24T01:48:03Z 2018-08-24T01:48:03Z 2007-12-01 Conference Paper Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol.4400 LNCS, No.PART 2 (2007), 206-224 16113349 03029743 2-s2.0-38149139399 https://repository.li.mahidol.ac.th/handle/123456789/24390 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=38149139399&origin=inward |
institution |
Mahidol University |
building |
Mahidol University Library |
continent |
Asia |
country |
Thailand Thailand |
content_provider |
Mahidol University Library |
collection |
Mahidol University Institutional Repository |
topic |
Computer Science Mathematics |
spellingShingle |
Computer Science Mathematics Puntip Pattaraintakorn Nick Cercone Kanlaya Naruedomkul Hybrid rough sets intelligent system architecture for survival analysis |
description |
Survival analysis challenges researchers because of two issues. First, in practice, the studies do not span wide enough to collect all survival times of each individual patient. All of these patients require censor variables and cannot be analyzed without special treatment. Second, analyzing risk factors to indicate the significance of the effect on survival time is necessary. Hence, we propose "Enhanced Hybrid Rough Sets Intelligent System Architecture for Survival Analysis" (Enhanced HYRIS) that can circumvent these two extra issues. Given the survival data set, Enhanced HYRIS can analyze and construct a life time table and Kaplan-Meier survival curves that account for censor variables. We employ three statistical hypothesis tests and use the p-value to identify the significance of a particular risk factor. Subsequently, rough set theory generates the probe reducts and reducts. Probe reducts and reducts include only a risk factor subset that is large enough to include all of the essential information and small enough for our survival prediction model to be created. Furthermore, in the rule induction stage we offer survival prediction models in the form of decision rules and association rules. In the validation stage, we provide cross validation with ELEM2 as well as decision tree. To demonstrate the utility of our methods, we apply Enhanced HYRIS to various data sets: geriatric, melanoma and primary biliary cirrhosis (PBC) data sets. Our experiments cover analyzing risk factors, performing hypothesis tests and we induce survival prediction models that can predict survival time efficiently and accurately. © Springer-Verlag Berlin Heidelberg 2007. |
author2 |
King Mongkut's Institute of Technology Ladkrabang |
author_facet |
King Mongkut's Institute of Technology Ladkrabang Puntip Pattaraintakorn Nick Cercone Kanlaya Naruedomkul |
format |
Conference or Workshop Item |
author |
Puntip Pattaraintakorn Nick Cercone Kanlaya Naruedomkul |
author_sort |
Puntip Pattaraintakorn |
title |
Hybrid rough sets intelligent system architecture for survival analysis |
title_short |
Hybrid rough sets intelligent system architecture for survival analysis |
title_full |
Hybrid rough sets intelligent system architecture for survival analysis |
title_fullStr |
Hybrid rough sets intelligent system architecture for survival analysis |
title_full_unstemmed |
Hybrid rough sets intelligent system architecture for survival analysis |
title_sort |
hybrid rough sets intelligent system architecture for survival analysis |
publishDate |
2018 |
url |
https://repository.li.mahidol.ac.th/handle/123456789/24390 |
_version_ |
1763492056476418048 |