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@article{204050,
        author = {Ms.S.visali and Mr.J.Lin Eby Chandra},
        title = {CASUAL DISCOVERY AND EXPLAINABLE DIAGNOSIS SYSTEM FOR MEDICAL DATA},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {1357-1360},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204050},
        abstract = {Most AI-based diagnostic tools in healthcare are built on statistical correlations, which can produce misleading results when hidden variables influence the data. This work proposes a medical diagnosis framework that combines causal discovery with explainable AI to address that gap. Causal relationships among patient risk factors and outcomes are identified using the PC algorithm, LiNGAM, and DoWhy, and represented as Directed Acyclic Graphs to make the reasoning transparent. The system integrates Explainable AI (XAI), which can help users understand and interpret such autonomous predictions, helping to restore the users’ trust as well as making the decision-making process of such systems transparent. The addition of the XAI layer on top of the Machine Learning models in an autonomous system can also work as a decision support system for medical practitioners to aid the diagnosis process. In this research paper, we have analyzed the two most popular model explainers, Local Interpretable Model- agnostic Explanations (LIME) and SHAPley Additive explanations (SHAP), for their applicability in autonomous disease prediction.},
        keywords = {PC algorithm, LiNGAM, and DoWhy, analyze clinical datasets and construct Directed Acyclic Graphs (DAGs), Explainable AI (XAI) methods like SHAP and LIME.},
        month = {June},
        }