Advancing Chronic Kidney Disease Diagnosis: A Predictive Model Using Random Forest Classifier

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Polishetty Pranay
Mandadi Rahul Reddy
K Venkatesh Sharma


In the contemporary medical landscape, Chronic Kidney Disease (CKD) poses substantial challenges, often remaining undetected until severe damage ensues due to current systems' diagnostic limitations. Traditional methods grapple with issues like delayed diagnosis and intensive resource utilization, creating a pressing need for an advanced, efficient approach. Addressing this, our research introduces a ground-breaking predictive model using a Random Forest Classifier, tailored for early CKD detection. We meticulously pre-processed our data, ensuring its reliability, and employed the Random Forest method, known for its precision and ability to manage complex datasets. The model's performance, tested against a comprehensive dataset, achieved an extraordinary accuracy of 95%, highlighting its proficiency in early risk identification and potential in revolutionizing CKD management. This study signifies a remarkable stride in healthcare, offering a precise, scalable, and economical solution for CKD early intervention. By successfully pinpointing CKD onset at initial stages, our model facilitates prompt medical response, enhancing patient prognosis and reducing associated healthcare burdens. Furthermore, it sets the stage for extensive AI integration in diagnostic practices, promising substantial improvements in preventive care and health system efficiency. The implementation of this predictive tool is poised to significantly diminish CKD-related complications and fatalities, emphasizing machine learning's transformative impact in advancing global health standards. This model's integration represents a monumental leap in medical diagnostics, combining innovative technology with profound healthcare implications.

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Polishetty Pranay, Mandadi Rahul Reddy, and K Venkatesh Sharma, “Advancing Chronic Kidney Disease Diagnosis: A Predictive Model Using Random Forest Classifier”, Int. J. Comput. Eng. Res. Trends, vol. 10, no. 10, pp. 1–7, Oct. 2023.
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