An Empirical Evaluation of Ensemble Machine Learning Techniques for Chronic Kidney Disease Prophecy

The most intriguing task in life is vaticination in the medical field. Generally, Chronic Kidney Disease (CKD) is defined by a patient's urine abnormalities, structural abnormalities, or bloodied excretory, level of waste products. This detection can be achieved by applying Machine Learning algorithms like Random Forest, Decision Tree, KNN, and SVM on patient information to predict the occurrence of chronic kidney disease. This research focuses on the detection of kidney disease using a combination of multiple algorithms to improve accuracy and reliability. The dataset containing clinical features related to kidney diseases is preprocessed, and various machine-learning processes are applied individually. To enhance classification performance, an ensemble technique is employed, combining the predictions of these algorithms using voting or weighted averaging. The machine learning algorithms provide better operation of the clinical data and accurate vaticination of the habitual order complaints.