Authors:
Hafsaa Ouifak
1
and
Ali Idri
1
;
2
Affiliations:
1
Faculty of Medical Sciences, Mohammed VI Polytechnic University, Ben Guerir, Morocco
;
2
Software Project Management Research Team, ENSIAS, Mohammed V University, Rabat, Morocco
Keyword(s):
Explainable AI, Interpretability, Black-Box, Machine Learning, Fuzzy Logic, Neuro-Fuzzy, Medicine.
Abstract:
Machine Learning (ML) solutions have demonstrated significant improvements across various domains. However, the complete integration of ML solutions into critical fields such as medicine is facing one main challenge: interpretability. This study conducts a systematic mapping to investigate primary research focused on the application of fuzzy logic (FL) in enhancing the interpretability of ML black-box models in medical contexts. The mapping covers the period from 1994 to January 2024, resulting in 67 relevant publications from multiple digital libraries. The findings indicate that 60% of selected studies proposed new FL-based interpretability techniques, while 40% of them evaluated existing techniques. Breast cancer emerged as the most frequently studied disease using FL interpretability methods. Additionally, TSK neuro-fuzzy systems were identified as the most employed systems for enhancing interpretability. Future research should aim to address existing limitations, including the c
hallenge of maintaining interpretability in ensemble methods
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