Authors:
Hajar Hakkoum
1
and
Ali Idri
1
;
2
Affiliations:
1
SPM, ENSIAS, Mohammed V University, Rabat, Morocco
;
2
Faculty of Medical Sciences, Mohammed VI Polytechnic University, Ben Guerir, Morocco
Keyword(s):
Interpretability, Black Box, Machine Learning, Data Mining, Categorical Encoding, XAI.
Abstract:
This study explores the challenge of opaque machine learning models in medicine, focusing on Support Vector Machines (SVMs) and comparing their performance and interpretability with Multilayer Perceptrons (MLPs). Using two medical datasets (breast cancer and lymphography) and three encoding methods (ordinal, one-hot, and dummy), we assessed model accuracy and interpretability through a decision tree surrogate and SHAP Kernel explainer. Our findings highlight a preference for ordinal encoding for accuracy, while one-hot encoding excels in interpretability. Surprisingly, dummy encoding effectively balanced the accuracy-interpretability trade-off.