International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Chronic Kidney Disease (CKD) is a progressive medical condition characterized by the gradual loss of kidney function over time.This paper presents a machine learning–based approach to predict CKD using clinical . The study focuses on data preprocessing techniques, including handling missing values, feature scaling, and encoding categorical variables, to enhance model accuracy and reliability. Several machine learning algorithms, such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine, are implemented and evaluated using performance metrics like accuracy, precision, recall, and F1-score. Among these models, the Random Forest classifier demonstrates superior predictive performance, achieving high accuracy and robust generalization across test data. The experimental results suggest that the integration of machine learning techniques in healthcare can significantly assist medical practitioners in early CKD detection, risk stratification, and informed clinical decision-making. Furthermore, this study highlights the potential of artificial intelligence to transform traditional diagnostic procedures into data-driven, automated systems for improved healthcare delivery. and speed of disease diagnosis. The outcomes of this study highlight the potential of artificial intelligence (AI) in supporting data-driven healthcare solutions and enabling early intervention strategies for patients at risk of CKD

Keywords: Chronic Kidney Disease (CKD) Kidney function prediction Machine Learning (ML) Medical data analysis Disease classification Health informatics.

Downloads: | DOI: 10.17148/IJARCCE.2025.141005