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Artificial Intelligence (AI) has made significant strides in revolutionizing healthcare, offering unparalleled opportunities for improved diagnostics and decision-making. The use of AI in healthcare sector is becoming more popular in today's era. In recent times, eXplainable AI (XAI) has shown significant improvement in medical diagnosis. Doctors are also validating patient test reports via XAI predictions. However, these intelligent systems pose challenges with regards to the underlying understanding and interpretations of AI models. In other words, it is essential to investigate the reasons when these models make certain predictions. The proposed work aims to improve the interpretability of various AI models by employing 2 techniques, Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). The study uses the Random Forest Classifier on the Breast Cancer Wisconsin (Diagnostic) dataset. The findings of this study are expected not only to advance AI technologies in healthcare but also build trust in doctors and other healthcare experts.
Jain et al. (Thu,) studied this question.
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