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@article{164718,
        author = {Ch. Papa rao and Syed Amjad and MADDIGARI RUPA VISWANATH and Shaik Mansoor and Gunji Vasavi},
        title = {KIDNEY DISEASE PREDICTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {2574-2579},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164718},
        abstract = {A serious condition that can last a lifetime, chronic kidney disease (CKD) is brought on by either impaired kidney function or kidney cancer. It is possible to stop or limit the advancement of this chronic illness to the point when a patient's sole options for survival are dialysis or surgery. An earlier diagnosis and the right treatment can make this more likely to occur. The potential of various distinct machine learning techniques for offering an early diagnosis of chronic kidney disease (CKD) has been examined throughout this study. On this subject, a substantial quantity of research has been done. Nevertheless, by utilizing predictive modelling, we are strengthening our strategy. As such, in our methodology, we explore the relationship between data elements and target class features. Because predictive modelling allows for the introduction of improved attribute measures, we may use machine learning and predictive analytics to build a collection of prediction models.},
        keywords = {Chronic Kidney Disease, Predictive Modelling, Data Elements, Target Class Features.},
        month = {},
        }