TY - GEN
T1 - Predicting quality of life for lung transplant recipients
T2 - 2017 IEEE Smart Grid Conference, SGC 2017
AU - Al-Ebbini, Lina
AU - Oztekin, Asil
AU - Sevkli, Zulal
AU - Delen, Dursun
PY - 2018/1/12
Y1 - 2018/1/12
N2 - 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.
AB - 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.
KW - decision support systems
KW - feature selection
KW - genetic algorithms
KW - quality of life
KW - UNOS lung allocation
UR - http://www.scopus.com/inward/record.url?scp=85049077545&partnerID=8YFLogxK
U2 - 10.1109/AEECT.2017.8257741
DO - 10.1109/AEECT.2017.8257741
M3 - Conference contribution
T3 - 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2017
SP - 1
EP - 6
BT - 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2017
A2 - Al-Oqily, Ibrahim
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 December 2017 through 21 December 2017
ER -