We propose a novel method based on associative classification in combination with odds ratios, a well-understood epidemiological metric, as an interpretable method for in-hospital mortality estimation, which is influenced by thousands of clinical variables.
We tested and validated the method for cases in intensive and emergency care.
The resulting model achieves an area under the receiver operating characteristic curve of 0.98.
The model is easy to interpret in the form of one-to-one rules and the corresponding odds ratios.
This study shows that associative classification combined with epidemiological metrics can be used as effective and interpretable machine learning models in the presence of outcomes that are influenced by thousands of variables.