Chronic Kidney Disease (CKD) Diagnosis using Machine Learning Methodology Classifications

Abstract

Early diagnosis of kidney as well as pre-kidney disease is crucial for patients because it allows them to take control of their condition and could potentially avoid or delay more significant consequences that could lower their quality of life. The chance of developing a major disease might be decreased with its assistance. Almost every part of the body could be impacted by chronic kidney disease (CKD). Fluid retention in the lungs, high blood pressure, and swelling of the legs and arms are all potential side effects. This study proposes a model that makes use of machine learning (ML) algorithms for diagnosing kidney disease. The preprocessing dataset, which contains missing values and is preprocessed with the use of mean, delete, and median approaches before data scaling, is the foundation of the suggested model. To achieve the highest classification accuracy, the preprocessing stage receives the results of missing values. Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are the two classification algorithms used to classify whether kidney disease is present or absent. Classify the dataset into testing and training (40% and 60%, respectively). The accuracy, F1-score, recall, and precision have been utilized for evaluating the suggested model. The kidney disease data-set has been used to test the outcomes of the suggested model. Without preprocessing any missing values in the dataset, the algorithms SVM and K-NN obtained maximum accuracy (95% and %89). Through deleting missing values from the dataset, the algorithms SVM and K-NN obtained maximum accuracy (%96 and %93). K-NN and SVM algorithms reached a maximum accuracy of %98 when using a mean technique; when using a median method, such algorithms attained an accuracy ranging from %95 to %98.