Title :
A framework for automatic modelling of survival using fuzzy inference
Author :
Hamdan, Hazlina ; Garibaldi, Jonathan M.
Author_Institution :
Intell. Modelling & Anal. (IMA) Res. Group, Univ. of Nottingham, Nottingham, UK
Abstract :
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.
Keywords :
cancer; data analysis; fuzzy reasoning; fuzzy set theory; knowledge based systems; medical computing; pattern clustering; surgery; ANFIS fuzzy inference system; analytical method; automatic modelling survival; conditional event probability; equalizer partitioning; expert knowledge; fuzzy c-means clustering; fuzzy inference structure; gradient descent; medical data analysis; membership function; nonnegative least square algorithm; operative surgery; ovarian cancer; patients; prognostic factors; replication data; rule base; survival analysis; survival curves; Adaptation models; Artificial neural networks; Cancer; Estimation; Hazards; Training; Adaptive Network Fuzzy Inference System (ANFIS); Nonnegative Least Squares (NNLS); Ovarian Cancer; Survival Analysis;
Conference_Titel :
Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1507-4
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZ-IEEE.2012.6251359