Advancing Chronic Kidney Disease Prediction through Machine Learning and Deep Learning with Feature Analysis

Abstract

Chronic Kidney Diesease (CKD) is a significant health issue, ranking as the fourth leading cause of mortality worldwide. The traditional diagnosis and treatment process, reliant on medical experts, is time-consuming. Therefore, thereis an urgent need for more efficient diagnostic methods to improve patient outcomes and reduce mortality rates. In this study, we employ Machine Learning (ML) and Deep Learning (DL) techniques to predict CKD based on important features. Feature analysis was performed using a correlation matrix and the LASSO algo-rithm to identify the most relevant features for model training. We evaluated several ML and DL classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbor (KNN), Decision Tree (DT), XG-Boost (XGB), Ada- Boost(AB), and Deep Neural Network.Our results demonstrated that the RF, LR, and SVM classifiers achieved the highest performance, with accuracy rates of 96.66%, 96.66% and 96%on average, respectively. This study identifies the most effective classifiers for CKD prediction and proposes a public modelto facilitate early diagnosis, potentially reducing CKD-related mortality