Early Prediction of Chronic Kidney Disease Using Machine Learning Approaches Chronic Kidney Disease Prediction

Abstract

Chronic Kidney Disease (CKD), also known as chronic renal disease. It is defined as a problem in the struc ture and function of the kidney. It is a progressive disorder and a life-threatening condition that often remains undiagnosed in its stages due to the absence of clear symptoms. Early CKD prediction utilizing clinical infor mation from a Kaggle dataset that is accessible to the public. The purpose of this study is to develop a machine learning-based framework for the early prediction of CKD. The following uses clinical data obtained from a pub licly available Kaggle dataset. The methodology involves loading the dataset followed by data preprocessing, including handling missing values and data normalization. Techniques for feature selection are applied to the dataset to identify the most relevant clinical attributes, and principal component analysis is employed in the pro cess of dimensionality reduction to enhance model efficiency and reduce redundancy to improve the process. The processed data are then used to train and evaluate machine learning classifiers. The proposed approach achieves an accuracy of 96 percent, hence proving that this approach gives effective performance in predicting CKD at an early stage. The results indicate that the integration of feature selection and PCA significantly improves model performance. The study indicates the capabilities of machine learning approaches as valid decision-support tools for early CKD prediction, which can assist healthcare professionals in improving patient outcomes are reducing disease progression