Published Paper Details:

Chronic Kidney Disease is a critical global health issue that demands accurate and timely diagnosis. The proposed system, CKD-Predict, leverages advanced machine learning algorithms such as Random Forest and XG Boost to provide reliable predictions. Utilizing the CKD dataset, it emphasizes rigorous data cleaning and preprocessing to handle missing values effectively. With a user-friendly web interface built using Flask, CKD-Predict ensures real-time accessibility for medical practitioners and healthcare institutions. This proposed system's adaptive machine learning backend continuously improves its prediction accuracy as more data is incorporated, making it a robust and evolving tool. By facilitating early detection and intervention, the proposed system assists healthcare professionals in identifying patients at risk, ultimately contributing to better management and outcomes for CKD cases. With its ability to process data efficiently and provide actionable insights, the proposed system aims to empower the medical community and improve the quality of life for CKD patients worldwide.