Early Detection of Chronic Kidney Disease (CKD)Using Machine Learning Algorithms

Abstract

Chronic Kidney Disease (CKD) is a significant global health challenge, with early detection being crucial for effective treatment and management. This study investigates the application of machine learning algorithms to enhance the early diagnosis of CKD. A dataset of patient records was utilized to evaluate the performance of various machine learning models, including Decision Trees, Random Forests, and Support Vector Machines (SVM). The dataset underwent preprocessing to address missing values and normalization to ensure consistency. Feature selection techniques were employed to identify the most relevant attributes for accurate prediction. The results indicate that Random Forests achieved the highest accuracy of 95% in detecting CKD, outperforming other models. Key features such as serum creatinine and blood pressure were found to be critical predictors of CKD. The findings suggest that machine learning algorithms, particularly Random Forests, can significantly improve the early detection of CKD, potentially reducing the burden on healthcare systems and enhancing patient outcomes. This research con tributes to the growing body of knowledge on the application of artificial intelligence in healthcare and provides a foundation for future studies on the real-world deployment and clinical validation of machine learning models for CKD diagnosis.