Table 1 Details of some of the breast cancer survival prediction studies in recent past years.
From: Breast cancer survival prognosis using the graph convolutional network with Choquet fuzzy integral
Author | Year | Modality | Prediction model |
|---|---|---|---|
Gevaert et al.5 | 2006 | CLN, GE | Bayesian network |
Sun et al4 | 2007 | CLN, GE | Correlation based classifier |
Xu et al.6 | 2012 | GE | SVM with a recursive feature reduction |
Nguyen et al7 | 2013 | Image | Random forest (RF) |
Sun et al.9 | 2018 | GE, HistoIm | GPMKL (multiple kernel learning ) |
Sun et al.8 | 2018 | CLN, GE, CNA | Multimodal deep neural network |
Arya et al.12 | 2020 | CLN, GE, CNA | Deep-learning based STACKED ensemble |
Hsu et al.10 | 2021 | CLN, GE | Ensemble learning with bimodal DNN |
Arya et al.11 | 2021 | CLN, GE, HistoIm. | Gated attentive deep learning with RF |
Arya et al.13 | 2021 | CLN, GE, CNA | Generative incomplete multi-view prediction (GIMPP) |
Arya et al.14 | 2022 | CLN, GE, CNA, HistoIm. | SVM based on utility kernel |
Arya et al.15 | 2023 | CLN, GE, CNA, DNAMe, | PCA and VAE with |
mRNASeq, HistoIm | RF and SVM | ||
Du et al.16 | 2023 | CLN, GE, CNA | Multi-scale bilinear convolutional neural network |
