An Early Prediction Model for Chronic Kidney Disease Using Machine Learning

Chronic kidney disease (CKD) or chronic renal disease-has become a major issue with a steady growth rate. A person can survive for a maximum of 18 days, which makes a huge demand for a kidney transplant and dialysis. It is necessary to have a good model to predict this disease at an earlier stage. It can be identified using ML models. This proposal proposes a workflow to predict CKD status based on the pre-processing steps of clinical data collection, incorporating data, handling missing values with collaborative filters, and attribute selection. This proposal used seven machine models and will compare all the models and the extra tree classifier and decision tree to ensure high accuracy and minimal bias for the attribute. This research also focuses on the real-time aspects of data collection and highlights the importance of domain knowledge when using machine learning for CKD status prediction. The evolution of the proposed model shows that the model can predict CKD with an accuracy of 98.65%.