Chronic Kidney Disease Detection Using Machine Learning Approach

Abstract

Chronic kidney disease is a critical and dangerous medical condition that can lead to many problems if it is not treated properly or detected at an early stage. It is a medical condition that can also lead to kidney failure. The waste and extra fluids present in the blood are removed by the kidneys and then passed from body through urine. The body may accumulate hazardous amounts of electrolytes, fluids, and waste if you reach the last stages of chronic renal disease. Because kidney failure does not initially manifest any symptoms, the beginning date may not be identified, and the patient's sickness may not even be recognized. We must identify the patients with chronic kidney disease early so that treatment can begin in order to prevent or slower the advancement of the disease and prevent the emergence of other related issues. To overcome this situation, we have developed a system to detect the disease using preprocessing of data, feature selection, and machine learning algorithms for which Logistic Regression, Extreme Gradient Boosting, Random Forest, Support Vector Machine, Decision Tree, and Naive Bayes are used. The accuracy of these algorithms is analyzed and compared to predict the disease precisely. The algorithm which has provided the best results is implemented for the disease prediction. We have enhanced the performance and effectiveness of the model by removing unnecessary attributes from the dataset and only gathering those that are most beneficial.