Comparative Analysis of Machine Learning Algorithms for Early Detection of Chronic Kidney Disease

Chronic kidney disease (CKD) is a significant public health issue with a high morbidity rate. Machine learning (ML) algorithms have shown potential for improving the detection and prediction of CKD. In this study, four popular ML models, namely naive bayes, support vector machine (SVM), KNN, and linear regression, were used for the active prediction of CKD. A CKD dataset from Kaggle was preprocessed to improve performance. The accuracy (Acc%), precision (Prcsn), recall (Rcll), and F1 score (F1 scr) were calculated for each ML model. The SVM model outperformed the other models with an accuracy of 99.04%. This study suggests that ML algorithms have the potential to improve the prediction of CKD, with SVM being the most effective model. The results of this study can potentially lead to the development of improved CKD prediction models, aiding in early diagnosis and treatment of the disease, ultimately improving patient outcomes, and reducing the burden on the healthcare system.