Predicting quality of life for lung transplant recipients: A hybrid genetic algorithms-based methodology

Lina Al-Ebbini, Asil Oztekin, Zulal Sevkli, Dursun Delen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Feature selection, a critical pre-processing step for data mining, is aimed at determining representative variables/predictors from a large and feature-rich dataset for development of an effective prediction model. The purpose of this paper is to develop a hybrid methodology for feature selection using genetic algorithms to identify such representative features (input variables) and thereby to ensure the development of the best possible analytic model to predict and explain the target variable, quality of life (QoL), for patients undergoing a lung transplant overseen by the United Network for Organ Sharing (UNOS). The evaluation of three classification models, GA-kNN, GA-SVM, and GA-ANN, demonstrated that performance of the lung transplantation process has significantly improved via the GA-SVM approach, although the other two models have also yielded considerably high prediction accuracies. This study is unique in that it proposes a hybrid GA-based feature selection methodology along with design and development of several highly accurate classification algorithms to identify the most important features in the large and feature rich UNOS transplant dataset for lung transplantation.

Original languageEnglish
Title of host publication2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2017
EditorsIbrahim Al-Oqily
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781509059690
DOIs
StatePublished - 12 Jan 2018
Externally publishedYes
Event2017 IEEE Smart Grid Conference, SGC 2017 - Tehran, Iran, Islamic Republic of
Duration: 20 Dec 201721 Dec 2017

Publication series

Name2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2017
Volume2018-January

Conference

Conference2017 IEEE Smart Grid Conference, SGC 2017
CountryIran, Islamic Republic of
CityTehran
Period20/12/1721/12/17

Keywords

  • decision support systems
  • feature selection
  • genetic algorithms
  • quality of life
  • UNOS lung allocation

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