Next-Gen Healthcare: Machine Learning-Based Chronic Kidney Disease Prediction

Abstract

Chronic Kidney Disease (CKD) is a major global health issue that is often detected at later stages due to a lack of early screening methods. Traditional diagnosis depends on laboratory tests and doctors' expertise, which can sometimes delay timely treatment. Machine Learning (ML) is transforming healthcare by improving early disease detection and prediction accuracy. This study focuses on ML-based models for CKD prediction, using patient medical records and clinical data to improve diagnosis. Different ML techniques, including Decision Trees, Random Forest, Support Vector Machines, and Deep Learning, are analyzed to find the most effective approach. To improve model performance, data preprocessing methods such as feature selection, normalization, and handling of missing values are applied. The findings show that ML models perform much better than traditional diagnostic methods, achieving high accuracy, precision, recall, and AUC-ROC scores. Ensuring that these models are interpretable and explainable is essential for their practical use in healthcare. While challenges like data imbalance and biases exist, ML-based CKD prediction systems offer great potential for real-world applications, allowing for early detection and personalized treatment. This research highlights how ML can revolutionize healthcare by enabling automated, data-driven diagnostic solutions. Future improvements will focus on strengthening model reliability, exploring deep learning methods, and integrating ML predictions into clinical decision-making tools. Advancing AI-driven healthcare solutions can significantly improve patient care, reduce CKD-related complications, and lower mortality rates.