Multi-objective evolutionary algorithms for fuzzy classification in survival prediction

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M.

doi:10.1016/j.artmed.2013.12.006

Cited by 54 publications

(58 citation statements)

References 66 publications

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“…Regarding the ICBU, few works have dealt with the problem of survival prediction using machine learning or intelligent data analysis [8]. As far as we know, none of them consider patient's evolution.…”

Section: Background Workmentioning

confidence: 99%

Using Multivariate Sequential Patterns to Improve Survival Prediction in Intensive Care Burn Unit

Casanova

Campos

Fernández-Fernández-Arroyo

et al. 2015

Artificial Intelligence in Medicine

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Abstract. Resuscitation and stabilization are key issues in Intensive Care Burn Units and early survival predictions help to decide the best clinical action during these phases. Current survival scores of burns focus on clinical variables such as age or the body surface area. However, the evolution of other parameters (e.g. diuresis or fluid balance) during the first days is also valuable knowledge. In this work we suggest a methodology and we propose a Temporal Data Mining algorithm to estimate the survival condition from the patient's evolution. Experiments conducted on 480 patients show the improvement of survival prediction.

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Section: Background Workmentioning

confidence: 99%

Using Multivariate Sequential Patterns to Improve Survival Prediction in Intensive Care Burn Unit

Casanova

Campos

Fernández-Fernández-Arroyo

et al. 2015

Artificial Intelligence in Medicine

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“…In this paper we apply a wrapper feature selection mechanism via the adaptation of a multi-objective evolutionary algorithm known as ENORA [9], [10], and we compare its performance against the classical multi-objective evolutionary algorithm NSGA-II [11] along two directives: (i) performance of the multi-objective search strategy via measuring the hypervolume, and (ii) quality of the classifiers that have been built over the selected features. The goal is to build a classifier for the session data of a multi-skill contact center, which allows us to predict the outcome of a communication based on a limited set of features.…”

Section: Introductionmentioning

confidence: 99%

Attribute Selection Via Multi-Objective Evolutionary Computation Applied to Multi-Skill Contact Center Data Classification

Jiménez

Marzano²,

Sánchez

et al. 2015

2015 IEEE Symposium Series on Computational Intelligence

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Abstract-Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We propose the application of the multi-objective evolutionary algorithm ENORA to the task of feature selection for multi-class classification of data extracted from an integrated multi-channel multi-skill contact center, which include technical, service and central data for each session. Additionally, we propose a methodology to integrate feature selection for classification, model evaluation, and decision making to choose the most satisfactory model according to a a posteriori process in a multi-objective context. We check out our results by comparing the performance and the classification rate against the well-known multi-objective evolutionary algorithm NSGA-II. Finally, the best obtained solution is validated by a data expert's semantic interpretation of the classifier.

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“…Of those, 785 focused on the wrong topic (e.g., robotics) and were excluded from the analysis. Afterwards, titles and abstracts of 722 articles were screened, and a total of 15 studies were identified and included in the analysis [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. 4 studies [21,24,25,30] used the same database of wound images, and 3 studies [22,23,27] used the same patient dataset.…”

Section: Resultsmentioning

confidence: 99%

Machine learning in burn care and research: A systematic review of the literature

Liu

Salinas

2015

Burns

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Multi-objective evolutionary algorithms for fuzzy classification in survival prediction

Cited by 54 publications

References 66 publications

Using Multivariate Sequential Patterns to Improve Survival Prediction in Intensive Care Burn Unit

Using Multivariate Sequential Patterns to Improve Survival Prediction in Intensive Care Burn Unit

Attribute Selection Via Multi-Objective Evolutionary Computation Applied to Multi-Skill Contact Center Data Classification

Machine learning in burn care and research: A systematic review of the literature

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