Dietary prediction for patients with Chronic Kidney Disease (CKD) by considering blood potassium level using machine learning algorithms

Kidney damage and diminished function that lasts longer than three months is known as Chronic Kidney Disease (CKD). The primary goal of this research study is to identify the suitable diet plan for a CKD patient by applying the classification algorithms on the test result obtained from patients' medical records. The aim of this work is to control the disease using the suitable diet plan and to identify that suitable diet plan using classification algorithms. The suggested work pacts with the recommendation of various diet plans by using predicted potassium zone for CKD patients according to their blood potassium level. The experiment is performed on different algorithms like Multiclass Decision Jungle, Multiclass Decision Forest, Multiclass Neural Network and Multiclass Logistic Regression. The experimental results show that Multiclass Decision Forest algorithm gives a better result than the other classification algorithms and produces 99.17% accuracy.