Table 5 Summary of related work
From: Ada-WHIPS: explaining AdaBoost classification with applications in the health sciences
Author(s) | Date | Medical | Model | XAI |
|---|---|---|---|---|
| Â | Â | Condition(s) | Â | Mechanism |
Lamy et al. [47] | 2019 | Breast Cancer | WkNN and MDS | CBR |
| Â | Â | (treatment) | Â | Â |
Kwon et al. [48] | 2018 | General health | RNN | t-SNE and |
| Â | Â | Â | Â | Visual Analytics |
Adnan and Islam [31] | 2017 | Heart disease, | Tree ensembles | Logical Rules |
| Â | Â | dementia | Â | Â |
Jalali and Pfeifer [8] | 2016 | Cancer | L1-SVM | Feature |
| Â | Â | biomarkers | ensemble | importance |
Turgeman and May [12] | 2016 | Hospital | C5.0 Tree and SVM | Logical Rule |
| Â | Â | readmission | ensemble | Â |
Jovanovic et al. [11] | 2016 | Hospital | Tree Lasso | Regression |
| Â | Â | readmission | Â | Coefficients |
Letham et al. [13] | 2015 | Stroke | BRL | Bayesian Rules |
Caruana et al. [6] | 2015 | Pneumonia risk | GA2M | PI plots |
Kästner et al. [49] | 2012 | Breast cancer | Neural Gas | Fuzzy Rules |