Performance Evaluation of Various Machine Learning Algorithms in Prediction of Chronic kidney disease (CKD)

Abstract

With a high rate of morbidity and mortality as well as the ability to spread other diseases, chronic kidney disease (CKD) is a major worldwide health concern. Patients sometimes overlook the disease in the early stages of CKD since there are no evident symptoms. Early diagnosis of CKD enables patients to receive effective treatment in time to slow the disease's progression. Due to their quick and precise detection capabilities, machine learning models can help therapists accomplish this goal efficiently. In this research, we suggest a machine learning approach to CKD diagnosis. The website KAGGLE provided the CKD data set, which has a significant number of missing values.. The mean value is used to fill in the blanks; for object data types (strings), we utilized the most frequent object (string) to replace the missing values. Since patients may overlook particular measurements for a variety of reasons, missing values are typically observed in real-world medical scenarios. Four machine learning algorithms—Logistic Regression, SVM, Random Forest Classifier, and Decision Tree Classifier—were applied to create models after successfully completing the incomplete data set. Random Forest has the highest accuracy of these machine learning models.