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@article{206968,
        author = {Mohammad Iqbal and Abhishek Kumar and Rakesh Tanwar},
        title = {TabTransformer and Traditional Machine Learning Models for Reliable Chronic Kidney Disease Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {3177-3181},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206968},
        abstract = {This work improves the credibilty of early detection of chronic kidney disease by utilizing traditional Machine learning models. It also experiments the advanced Generative AI model, TabTransformer which is successful in attaining high accuracy. We have experimented with traditional machine learning models such as SVM, KNN, Random Forest, Gradient Boosting, Ada Boost, XG Boost, Decision Tree all of which are able to gain high accuracy in predicting the CKD. However, TabTransformer tops the table by attaining an accuracy of 98.5 percentage and F1-Score of 98.1 percentage. We have used real-clinical dataset CKD from Kaggle. This method is especially useful in rural areas where medical facilities are scarce and detecting disease early can help save the patient.},
        keywords = {Ensemble Learning, Chronic Kidney Disease Prediction, Machine Learning Models, Early Disease Detection, Clinical Decision Support},
        month = {July},
        }