A Filter Based Prediction Technique To Determine CKD Using Hwf-Fs, HWFE-Fs And EEA-DNN
Chronic Kidney Disease (CKD) is a global problem in the wellbeing of people. The CKD function is to control the blood and remove the volume of excess electrolytes, the total amount of water is steady and hormones are also made. Elevated blood pressure, anaemia, poor nutritional health and damage to nerves and someone can develop similar technical hitches to handle them. Recognition and action can also prevent CKD from being worse at an early point. Methods of data mining was applied to CKD. The data mining is considered as a process of using conventional knowledge, to decide the normal trends and to establish potential decisions, resulting from the convergence of quite a few current developments: the decrease in the cost of a huge database and the easy way of gathering information by means of network structure; the creation of machine learning techniques for processing this sort of data;Several data mining methods for forecasting CKD have recently been added. The purpose of this work is to co-relate the efficiency of the CKD prediction-based ELM classifier, HFWE-FS algorithm and EEAw-DNN algorithm depending on the parameters such as accuracy, precision, and error rate. From the results, the goal of the EEAw-DNN-based algorithm results is more accurate than HWFFS, the HWFE-FS algorithm.