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Using fuzzy sets in surgical treatment selection and homogenizing stratification of patients with significant chronic ischemic mitral regurgitation


Nikolova, N and Panayotov, P and Panayotova, D and Ivanova, S and Tenekedjiev, K, Using fuzzy sets in surgical treatment selection and homogenizing stratification of patients with significant chronic ischemic mitral regurgitation, International Journal of Computational Intelligence Systems, 12, (2) pp. 1075 - 1090. ISSN 1875-6891 (2019) [Refereed Article]

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2019 The Authors. Licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

DOI: doi:10.2991/ijcis.d.190923.002


We present three (main one and two auxiliary) fuzzy algorithms to stratify observations in homogenous classes. These algorithms modify, upgrade and fuzzify crisp algorithms from our earlier works on a medical case study to select the most appropriate surgical treatment for patients with ischemic heart disease complicated with significant chronic ischemic mitral regurgitation. Those patients can be treated with either surgical revascularization and mitral valve repair (group A) or with isolated surgical revascularization (group B) depending on their health status. The main algorithm results in a fuzzy partition of patients in two fuzzy sets (groups A and B) through identification of their degrees of membership. The resulting groups are highly non-homogenous, which impedes subsequent proper comparisons. So, the two auxiliary algorithms further stratify each group into two homogenous subgroups with comparatively preserved medical condition (A1 and B1) and with comparatively deteriorated medical condition (A2 and B2). Those two algorithms perform fuzzy partition of patients from A and B respectively into A1, A2, B1 and B2 by identifying their conditional degrees of membership to those subgroups. We then utilize the product t-norm to calculate the degree of membership of patients to their respective subgroup as an intersection of two fuzzy sets. We demonstrate how to form fuzzy samples for medical parameters for any subgroup. We also compare the performance of the fuzzy algorithms with their preceding crisp version, as well as with eight Bayesian classifiers. We then assess the quality of classification by modified confusion matrices, summarized further into four criteria. The fuzzy algorithms show total superiority over the other methods, and excellent differentiation of typical patients and outliers. On top, only the fuzzy algorithms provide a measure of how typical a patient is to its subgroup. The fuzzy algorithms clearly outline the role of the Heart Team, which is missing in the Bayesian classifiers.

Item Details

Item Type:Refereed Article
Keywords:fuzzy sets, medical studies, stratification of patients, algorithms
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Knowledge representation and reasoning
Objective Division:Health
Objective Group:Clinical health
Objective Field:Clinical health not elsewhere classified
UTAS Author:Nikolova, N (Professor Nataliya Nikolova)
UTAS Author:Tenekedjiev, K (Professor Kiril Tenekedjiev)
ID Code:135999
Year Published:2019
Web of Science® Times Cited:3
Deposited By:Maritime and Logistics Management
Deposited On:2019-11-25
Last Modified:2020-05-04
Downloads:19 View Download Statistics

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