Chronic Kidney Disease Identification Using Random Forest and XGBoost

Chronic Kidney Disease (CKD) is a serious and long term condition stemming from either renal disease or flawed kidney functions. Kidney cancer, known for its lethality, holds paramount importance in patient survival, necessitating early diagnosis and accurate classification. Timely intervention and suitable therapy can impede or postpone the progression of this chronic ailment to its advanced stages, where life-saving measures like dialysis or renal transplantation become imperative. The pressing challenge in current research revolves around the development of automated tools proficient in precisely identifying Chronic kidney Disease. This paper introduces the application of XGBoost and Random Forest algorithm which efficiently and effectively detect Chronic kidney disease, offering the potential to mitigate its progression and improve patient prognosis. The efficacy of classification technology is contingent upon the quality of the dataset.