HEART DISEASE
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Multi-class classification | Heart-disease Hungarian | F1-score93.08 | 16 | |
| Binary Classification | Heart Disease | AUC0.93 | 15 | |
| Private Decision Tree Evaluation | heart-disease | Online Running Time0 | 12 | |
| Classification | Heart Disease 25% held-out (test) | Accuracy0.6 | 12 | |
| Clustering | Heart Disease dataset (test) | Calinski Harabasz Score67.88 | 10 | |
| Classification | Heart Disease Cleveland 79 (test) | Average Cost0.089 | 7 | |
| Binary Classification | Heart disease Cleveland (test) | AUC0.91 | 7 | |
| Abductive Explanation Generation | Heart Disease (test) | Average Execution Time (ms)1.64 | 6 | |
| Binary Classification | Heart Disease Cleveland standard processed (train) | AUC0.8314 | 6 | |
| Manifold Learning | Heart Disease | (TW + CN)/293.5 | 5 | |
| Classification | Heart Disease Real World Incomplete (test) | Accuracy91.98 | 5 | |
| Classification | Heart Disease (10-fold CV) | P-Value0.468 | 5 | |
| Outlier Explanation | HEART DISEASE | MDL642 | 5 | |
| Decision Tree Evaluation | heart-disease | Overall Latency (s)2.31 | 4 | |
| Binary Classification | Heart Disease (test) | Macro F1 Score77.9 | 4 | |
| Imputation | Heart Disease | KS0.54 | 4 | |
| Data Imputation | Heart Disease | MI (dev)0.32 | 4 | |
| Data Imputation | Heart Disease | RMSE0.13 | 4 | |
| Abductive Explanation Generation | Heart Disease Rejected | Average Explanation Size2.79 | 3 | |
| Abductive Explanation Generation | Heart Disease | Avg Explanation Size2.63 | 3 |
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