Feature Selection AI Technique for Predicting Chronic Kidney Disease

Abstract

The kidney is a vital organ that plays a crucial role in eliminating waste and excess water from the bloodstream. When renal function is impaired, the filtration process also ceases. This leads to an elevation of harmful molecules in the body, a condition referred to as chronic kidney disease (CKD). Early-stage chronic kidney disease often lacks noticeable symptoms, making it challenging to detect in its early stages. Diagnosing chronic kidney disease (CKD) typically involves advanced blood and urine tests, but unfortunately, by the time these tests are conducted, the disease may already be life-threatening. Our research focuses on the early prediction of chronic kidney disease (CKD) using machine learning (ML) and deep learning (DL) techniques. Utilized a dataset from the machine learning repository at the University of California, Irvine (UCI) to train various machine learning algorithms in conjunction with a Convolutional Neural Network (CNN) model. The algorithms encompassed in this set are Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB). Based on the experimental results, the CNN model achieves a prediction accuracy of precisely 97% after feature selection, the highest among all models tested. Hence, the objective of this project is to develop a deep learning-based prediction model to aid healthcare professionals in the timely identification of chronic kidney disease (CKD), potentially leading to life-saving interventions for patients.