A Model for Smart Detection: Modified Explainable Machine Learning for Interpreting Detection Results - Alifia Revan Prananda; Frannita, Eka Legya (Science & Technology Asia, 2025)
Medical imaging analysis using artificial intelligence has become a powerful system for assisting the doctor in diagnosing some diseases. Most of CAD performed excellent performance with average accuracy of more than 80%. Regardless of the excellent performance of artificial intelligence in the CAD, implementation of artificial intelligence in medical cases is still causing controversy. It happened due to the black-box principle of AI. Actually, both machine learning and deep learning worked in the black-box direction in which it was difficult to recognize how the model performed and how it analyzed the data. Hence, it became controversial since there remained some big questions about “how can the doctor trust the AI result?” Regarding this problem, a continued solution was needed. In this study we proposed a modified SHAP for explaining the artificial intelligence result. The modification itself is conducted by performing correlation in the perturbation process of SHAP. Our proposed solution was performed into two different datasets to evaluate the significance and the reliability of the proposed solution. According to both the visual analysis and statistical test, we conclude that the proposed solution gave a more rational explanation compared to the original SHAP.