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@article{170467,
        author = {Kaviya and Sandhya and Davinsi Ragamalika and Lakshmipriya and Bhavani},
        title = {Enhanced Breast Cancer Prediction using Soft Computing and XAI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1349-1356},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170467},
        abstract = {Interpretable AI is critical for constructing trustworthy systems, particularly in sensitive areas like breast cancer analysis, where patient outcomes are at stake. Many current AI models prioritize complexity and predictive accuracy, often at the cost of interpretability. This paper introduces an integrated approach combining Fuzzy Inference Systems (FIS), SHAP (Shapley Additive Explanations), and Grad-CAM (Gradient-weighted Class Activation Mapping) to create more interpretable and reliable breast cancer prediction models. FIS provides rule-based reasoning, allowing clinicians to trace the AI's decision-making process step by step. SHAP offers insights into the impact of each feature, clarifying how factors such as age or tumor size contribute to the prediction. Grad-CAM produces visual heat maps highlighting key areas in medical images, such as mammograms, showing clinicians where the model focused during diagnosis. This integrated approach improves both transparency and accuracy, providing clinicians with a reliable, interpretable decision-support system. By making AI predictions in breast cancer analysis more understandable, this method enhances trust in AI-driven healthcare, ultimately leading to better patient outcomes through informed, reliable diagnostic tools.},
        keywords = {FIS, Grad-CAM, SHAP.},
        month = {December},
        }