Chronic Kidney Disease Prediction using Machine Learning Algorithms

Abstract

Diabetes and high blood pressure are the primary causes of Chronic Kidney Disease (CKD). A person with CKD has a higher chance of dying young. Doctors face a difficult task in diagnosing the different diseases linked to CKD at an early stage to prevent the disease. Early discovery of CKD empowers sufferers to get the opportunity remedy to decorate the motion of this infection. CKD is among the top 20 causes of death worldwide and affects approximately 10% of the world's adult population. CKD is a disorder that disrupts normal kidney function. The novelty of this study lies in developing a diagnosis system to detect chronic kidney diseases. This study focused on evaluating a dataset collected from 400 patients containing 24 features. The mean and mode statistical analysis methods were used to replace the missing numerical and nominal values. To choose the most important features, Recursive Feature Elimination (RFE) was applied. Three classification algorithms applied in this study were k-nearest neighbors (KNN), Random Forest Classifier (RFC), and Ada Boost Classifier (ABC). All the classification algorithms achieved promising performance. The RFC and ABC Algorithm outperformed all other applied algorithms, reaching an accuracy, precision, recall, and F1-score of 100% for all measures. Therefore, Machine Learning techniques are of great importance in the early detection of CKD. These techniques are supportive of experts and doctors in early diagnosis to avoid developing kidney failure.