Prediction of chronic kidney disease (CKD) using Data Science

Chronic kidney disease (CKD) is a perennial condition where the kidneys deteriorate and stop functioning gradually. This disease has become one of the major public health concerns worldwide. It is insidious, often recognizable only by laboratory abnormalities until its latest stages. The main motive of this work is to ascertain the existence of chronic kidney disease by imposing various classification algorithms on the patient medical record. This research work is primarily concentrated on finding the best suitable classification algorithm which can be used for the diagnosis of CKD based on the classification report and performance factors. Empirical work is performed on different algorithms like Support Vector Machine, Random Forest, XGBoost, Logistic Regression, Neural networks, Naive Bayes Classifier. The experimental results show that Random Forest and XGBoost give better results when compared to other classification algorithms and generates 99.29% accuracy.