Prognosis of Chronic Kidney Disease Using ML Optimization Techniques

Chronic Kidney Disease (CKD) is a significant hardship to the medical system due to its rising pervasiveness, risk of developing into final stages of renal disease, and poor diagnostic grade and death. Early diagnostics and therapeutics are highly preferred because it can prevent undesirable outcomes. Machine Learning (ML) methods have been used significantly more recently to diagnose illnesses and detect early symptoms. The current work outlines a method that includes feature extraction and data pre-processing for predicting CKD. The CKD dataset was analyzed from the UCI Machine Learning library using Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) classifiers. The findings of the generated base classifier model were then improved using the bagging ensemble approach. Recursive Feature Elimination with Cross-Validation (RFECV) and analysis of variance have been used to choose features. The comparative analysis showed that the proposed method, with an accuracy of 98.2%, is superior to other algorithms. Applying tenfold cross-validation, the model was evaluated. The overall accuracy of the model is significantly higher than that of preceding research, suggesting the proposed model is more reliable than earlier studies.