Machine Learning Models for Prediction and Classification in Chronic Kidney Disease

Abstract

A major worldwide health concern, chronic kidney disease (CKD) requires early detection and efficient classification to enhance patient outcomes. Using a variety of datasets that include clinical, demographic, and laboratory information, this study explores the use of different machine learning models designed for the prediction and categorisation of CKD. The main goal of the study is to compare the effectiveness of sophisticated machine learning approaches, including as decision trees, support vector machines, and neural networks, with conventional statistical methods in order to ascertain which is better at correctly detecting CKD stages and forecasting the course of the disease. The results show that when compared to traditional techniques, machine learning models perform better in terms of categorisation and forecast accuracy.Notably, the incorporation of feature selection approaches improves the efficiency and interpretability of the model, enabling the identification of important risk variables that contribute to chronic kidney disease. This study highlights how machine learning has the potential to revolutionise nephrology by enabling prompt interventions and individualised treatment plans. It also emphasises how crucial interdisciplinary cooperation is to the creation of predictive analytics frameworks that are easily incorporated into clinical practice. The findings point to a paradigm shift in the management of chronic illnesses like CKD towards data-driven healthcare solutions, which would eventually improve patient quality of life and lower healthcare expenses.