A machine-learning approach to survival time-event predicting: Initial analyses using stomach cancer data
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11110%2F20%3A10419983" target="_blank" >RIV/00216208:11110/20:10419983 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11130/20:10419983 RIV/00064203:_____/20:10419983
Result on the web
<a href="https://doi.org/10.1109/EHB50910.2020.9280301" target="_blank" >https://doi.org/10.1109/EHB50910.2020.9280301</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EHB50910.2020.9280301" target="_blank" >10.1109/EHB50910.2020.9280301</a>
Alternative languages
Result language
angličtina
Original language name
A machine-learning approach to survival time-event predicting: Initial analyses using stomach cancer data
Original language description
The problem of predicting whether an event of interest does occur, and if so, when it occurs for given values of independent variables, is one of the basic tasks in survival analysis. Cox proportional hazard model and its numerous variants are usually used to handle this task; however, they are limited by strict statistical assumptions. In this study, we - rather than estimate the event's hazard function - prefer to make direct predictions by decomposition of the two-dimensional dependent time-event variable, depicting the event occurrence and time-to-event, into these two components, overcoming the statistical limitations. While the first part for the event occurrence is considered as a classification task, the second part for the time-to-event estimation is assumed to be a regression task. The variable's parts are treated as the classification and regression tasks, therefore built using machine-learning algorithms such as logistic and linear regression, naïve Bayes classifiers, classification and regression trees, support vector machines and neural networks, and applied together with the Cox's model to stomach cancer data. The machine-learning modeling of both the decomposed survival time-event variable's parts seems to be a promising alternative to predictions based on the Cox's hazard modeling.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
30502 - Other medical science
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 8th E-Health and Bioengineering Conference, EHB 2020
ISBN
978-1-72818-803-4
ISSN
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e-ISSN
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Number of pages
4
Pages from-to
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Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
Piscataway, USA
Event location
Iasi, Romania
Event date
Oct 29, 2020
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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