Chronic Kidney Disease Prediction using Random Forest and Suggesting Suitable Diet Plan

Dr.D.Rathna Kishore^1, M.Keerthi Reddy², V.Pavani³, P.Madhumitha⁴

Associate Professor¹, B.Tech Students^2,3,4

^* Department of Information Technology

^** ANDHRA LOYOLA INSTITUTE OF ENGINEERING & TECHNOLOGY

Abstract- Chronic kidney disease (CKD) is a global health problem with high morbidity, mortality, and other illnesses. Kidney disease affects 1 in 10 people worldwide. As the number of CKD patients increases, effective predictors for early detection of CKD are needed. Therefore, a better diagnosis of chronic kidney disease is needed to prevent continued progression. Machine learning models help clinicians achieve this goal with their fast and accurate prediction capabilities. In this project, we are using Random Forest Algorithm to determine if an individual has been diagnosed with CKD or Not CKD. And Stage of kidney disease and  based on the person's potassium level a diet plan is suggested. The CKD dataset was taken from the Kaggle machine learning repository. This system helps predict the early detection of chronic kidney disease.

Keywords- Chronic Kidney Disease, Prediction, Diet Plan, Machine Learning, Random Forest, Classification, Potassium level.