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A framework for automatic modelling of survival using fuzzy inference.

Survival analysis describes the analysis of data that corresponds to the time from when an individual enters a study until the occurrence of some particular event or end-point. It is most commonly used in the context of modelling survival (or disease-free interval time) in medical contexts, often co...

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Main Authors: Hamdan, Hazlina, Garibaldi, Jonathan M.
Format: Conference or Workshop Item
Language:English
English
Published: 2012
Online Access:http://psasir.upm.edu.my/id/eprint/27765/1/ID%2027765.pdf
http://psasir.upm.edu.my/id/eprint/27765/
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spelling my.upm.eprints.277652014-06-19T01:44:42Z http://psasir.upm.edu.my/id/eprint/27765/ A framework for automatic modelling of survival using fuzzy inference. Hamdan, Hazlina Garibaldi, Jonathan M. Survival analysis describes the analysis of data that corresponds to the time from when an individual enters a study until the occurrence of some particular event or end-point. It is most commonly used in the context of modelling survival (or disease-free interval time) in medical contexts, often concerned with the comparison of survival for different combinations of risk factors and/or treatments. Analytical methods which are transparent to the clinicians in understanding and explaining individual inference need to be considered when dealing with such medical data. In this paper, we present a framework for modelling survival utilising the application of the ANFIS fuzzy inference system. In this framework, alternative methods of partitioning the input space can be selected to define the membership functions, for example by using expert knowledge, equalizer partitioning, fuzzy c-means clustering, or the combination of these techniques. Further, the rule base can be established by enumerating all possible combinations of membership functions of all inputs. After the initialisation of the fuzzy inference structure, the replication data (until time to event) will be subject to be trained using the gradient descent and nonnegative least square algorithm to estimate the conditional event probability. This framework is validated over a novel dataset of patients following operative surgery for ovarian cancer. We demonstrate that the proposed framework can be successfully applied to estimate the hazard and survival curves between different prognostic factors, and model survival times, while providing models with explicit explanation capabilities. 2012 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/27765/1/ID%2027765.pdf Hamdan, Hazlina and Garibaldi, Jonathan M. (2012) A framework for automatic modelling of survival using fuzzy inference. In: 2012 IEEE International Conference on Fuzzy Systems, 10-15 June 2012, Brisbane, Australia. (pp. 1-8). English
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
English
description Survival analysis describes the analysis of data that corresponds to the time from when an individual enters a study until the occurrence of some particular event or end-point. It is most commonly used in the context of modelling survival (or disease-free interval time) in medical contexts, often concerned with the comparison of survival for different combinations of risk factors and/or treatments. Analytical methods which are transparent to the clinicians in understanding and explaining individual inference need to be considered when dealing with such medical data. In this paper, we present a framework for modelling survival utilising the application of the ANFIS fuzzy inference system. In this framework, alternative methods of partitioning the input space can be selected to define the membership functions, for example by using expert knowledge, equalizer partitioning, fuzzy c-means clustering, or the combination of these techniques. Further, the rule base can be established by enumerating all possible combinations of membership functions of all inputs. After the initialisation of the fuzzy inference structure, the replication data (until time to event) will be subject to be trained using the gradient descent and nonnegative least square algorithm to estimate the conditional event probability. This framework is validated over a novel dataset of patients following operative surgery for ovarian cancer. We demonstrate that the proposed framework can be successfully applied to estimate the hazard and survival curves between different prognostic factors, and model survival times, while providing models with explicit explanation capabilities.
format Conference or Workshop Item
author Hamdan, Hazlina
Garibaldi, Jonathan M.
spellingShingle Hamdan, Hazlina
Garibaldi, Jonathan M.
A framework for automatic modelling of survival using fuzzy inference.
author_facet Hamdan, Hazlina
Garibaldi, Jonathan M.
author_sort Hamdan, Hazlina
title A framework for automatic modelling of survival using fuzzy inference.
title_short A framework for automatic modelling of survival using fuzzy inference.
title_full A framework for automatic modelling of survival using fuzzy inference.
title_fullStr A framework for automatic modelling of survival using fuzzy inference.
title_full_unstemmed A framework for automatic modelling of survival using fuzzy inference.
title_sort framework for automatic modelling of survival using fuzzy inference.
publishDate 2012
url http://psasir.upm.edu.my/id/eprint/27765/1/ID%2027765.pdf
http://psasir.upm.edu.my/id/eprint/27765/
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score 13.211869

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