Machine Learning Models for Analysis and Prediction of Chronic Kidney Disease

Chronic kidney disease is an abnormal human health disorder due to progressive the malfunction of the kidney. This may cause critical kidney damage and other health issues if it is not diagnosed in earlier stages. With the advancement of technology, it has become easier for doctors to understand whether a patient might suffer from a particular disease or not based on the different health parameters. In this project, a model based on a Machine Learning (ML) approach has been proposed which can help to detect if any person is suffering or might suffer from chronic kidney disease. The work is done on a dataset based on chronic kidney disease available on the UCI repository. The dataset is preprocessed using KNN Imputation and Mode Values and applied ML techniques such as such as Logistic regression, AdaBoost classifier, Decision trees, Random Forest, Gradient Boosting and Naïve Bayes were applied. We get better performance in Gradient Boost with an accuracy of 98.22%.