Machine learning approach using hierarchical decision- tree projection algorithm to predict the presence of chronic kidney disease

Chronic Kidney disease is one of the eminent diseases which is commonly seen the patients which the various ailments which results in the step by step failure of kidneys which may result in the fatality of the human. The Chronic Kidney disease which is precisely called as CKD in medical terms is predicted with various symptoms that evolves in the human body. Strategically predicting the CKD using the machine learning algorithm is the challenging proportion. This paper solves the issues of predicting the CKD using the Hierarchical Decision-Tree Projection Algorithm which takes the various clinical study results of the human body into the dataset format and is algorithmically evaluated. Through this method the whole study in completely evaluated with the various parameters which is obtained from the human study. The variations in the whole study with respect to the parametrical correlation are taken into consideration. The results are obtained from each parametrical evaluation and with the results the prediction and presence of the Chronic Kidney disease is evaluated. The Experimental results show the algorithmic evaluations are showing the comparatively high accuracy and performance.