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@article{191428,
        author = {Chandan G N and Jayashri M and Dr Smitha Kurian and Dr Krishna Kumar P R},
        title = {Early Prediction of Chronic kidney Disease in Autoimmune Disorder Patients Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6647-6651},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191428},
        abstract = {CKD is chronic condition it can lead to Severe kidney failure it causes undiscovered. develop chronic inflammation of the kidneys, making them vulnerable to CKD. Early discovery should be done so that effective measures can be taken against CKD. approach based on clinical features of patient with auto-immune disease is proposed in this paper. The proposed method may it considers the Logistic Regression model, SVM model, and Random Forest model. The results obtain and show the accuracy of the proposed model is 98%, so it is regarded as an early predicts the CKD. Early discovery should be done so that effective measures can be taken against CKD.
A machine learning approach based on clinical features of patients with autoimmune diseases is proposed in this paper. The proposed method may consider the Logistic Regression model, SVM model, and Random Forest model. The results obtained show that the accuracy of the proposed model is better, so it is regarded as an early predictor of CKD.},
        keywords = {Chronic Kidney Disease, Autoimmune Diseases, Machine Learning, Early Detection, Healthcare Analytics},
        month = {January},
        }