The Ensembles of Machine Learning Methods for Survival Predicting after Kidney Transplantation

被引:6
|
作者
Tolstyak, Yaroslav [1 ,2 ]
Zhuk, Rostyslav [3 ]
Yakovlev, Igor [2 ]
Shakhovska, Nataliya [4 ]
Ml, Michal Gregus [5 ]
Chopyak, Valentyna [1 ]
Melnykova, Nataliia [4 ]
机构
[1] Danylo Halytskyi Lviv Natl Univ, Clin Immunol & Allergol Dept, UA-79010 Lvov, Ukraine
[2] Lviv Reg Clin Hosp, Hosp Nephrol & Dialysis Dept, UA-79010 Lvov, Ukraine
[3] Danylo Halytskyi Lviv Natl Univ, Surg & Transplantat Dept, UA-79010 Lvov, Ukraine
[4] Lviv Polytech Natl Univ, Artificial Intelligence Dept, UA-79013 Lvov, Ukraine
[5] Comenius Univ, Fac Management, Dept Informat Syst, Odbojarov 10, Bratislava 81499, Slovakia
来源
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 21期
关键词
organ transplantation; Kapplan-Meier method; machine learning; ensemble; early risk prediction; GUIDE;
D O I
10.3390/app112110380
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Machine learning is used to develop predictive models to diagnose different diseases, particularly kidney transplant survival prediction. The paper used the collected dataset of patients' individual parameters to predict the critical risk factors associated with early graft rejection. Our study shows the high pairwise correlation between a massive subset of the parameters listed in the dataset. Hence the proper feature selection is needed to increase the quality of a prediction model. Several methods are used for feature selection, and results are summarized using hard voting. Modeling the onset of critical events for the elements of a particular set is made based on the Kapplan-Meier method. Four novel ensembles of machine learning models are built on selected features for the classification task. Proposed stacking allows obtaining an accuracy, sensitivity, and specifity of more than 0.9. Further research will include the development of a two-stage predictor.
引用
收藏
页数:18
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