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@article{175497,
        author = {Shaik Davood and Dr. R. Yamuna},
        title = {CNN2D and SHAP for Enhanced  Explainability in Drug  Safety Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3168-3177},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175497},
        abstract = {Machine and deep learning models are widely used for predicting future values or classifying objects. However, some organizations, particularly in the medical field, are hesitant to adopt these models for predicting drug side effects or effectiveness. To address this, Explainable AI (XAI) has been introduced, which provides insights into model predictions by identifying the most influential features. This study reviews various machine learning (ML) and deep learning (DL) algorithms—such as XGBoost, GraphCNN, and MLP—using the 'Drug Classification' dataset from Kaggle to predict the best drug for patients based on health conditions. The use of XAI tools like SHAP helps explain these models' predictions, enhancing transparency and fostering trust in AI applications within healthcare.},
        keywords = {CNN2D, SHAP, explainable artificial intelligence (XAI)},
        month = {April},
        }