Predictive Analysis in Nephrology: Evaluating Machine Learning Models for Chronic Kidney Disease Prediction

Abstract

Abstract: In the realm of healthcare, early detection and accurate diagnosis are critical for effective treatment, especially for diseases that are often undiagnosed until later stages. This research presents an Automated Classification System focused on Chronic Kidney Disease (CKD), a prevalent condition frequently undetected in its early stages. Leveraging advanced machine learning techniques, the study aims to overcome the traditional reliance on doctors' intuition and experience by providing a data-driven approach for CKD diagnosis. Utilizing a comprehensive dataset, the research explores various data pre-processing methods, including innovative approaches for handling missing values and data integrity, which surpass the conventional mean and mode-based imputation methods prevalent in existing literature. Through exploratory data analysis and the application of diverse machine learning algorithms, the study seeks to identify the most effective model for predicting CKD risk. The findings of this research have significant implications for enhancing clinical decisions, offering a path towards more accessible, efficient, and accurate diagnosis of Chronic Kidney Disease, ultimately contributing to improved patient outcomes and healthcare practices