Name
Title | ||
|---|---|---|
Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation |
Abstract | ||
|---|---|---|
•A novel model predicts early antibody-incompatible kidney transplant rejection.•The models were trained on a small dataset of pre-transplant characteristics.•Decision Tree and Random Forest classifiers achieved 85% accuracy.•The models identified key risk factors, including specific IgG subclass levels. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.bspc.2017.01.012 | Biomedical Signal Processing and Control |
Keywords | Field | DocType |
Machine Learning,Small data sets,Biomedical systems,Decision Tree,Random Forest,Kidney transplants,Antibody-mediated acute rejection | Organ transplantation,Decision tree,Transplant rejection,Subclass,Decision support system,Bioinformatics,Predictive modelling,Random forest,Medicine,Kidney transplantation | Journal |
Volume | ISSN | Citations |
52 | 1746-8094 | 7 |
PageRank | References | Authors |
0.54 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Torgyn Shaikhina | 1 | 16 | 1.59 |
| David Philip Lowe | 2 | 7 | 0.54 |
| Sunil Daga | 3 | 7 | 0.54 |
| D. Briggs | 4 | 10 | 1.10 |
| R. M. Higgins | 5 | 7 | 0.87 |
| N. A. Khovanova | 6 | 23 | 2.72 |