Chronic kidney disease study to approach emerging endorsements for machine learning techniques in healthcare has become a real world emerging for obtaining accurate results of medical diagnosis, using the machine learning techniques involved the collaborative healthcare is developing interested in a hopeful field for successful outcomes with reducing costs. Thus, system can improve the efficiency of mining risk factors of chronic kidney disease, the structure consuming machine learning methods over large volume of dataset for making better decision and prediction. The random forest feature selection is the good method for feature selection, which takes less time compares to the other feature selection methods. The reports are tested using machine learning algorithms, to predict the chronic kidney disease. Random forest decision tree classification algorithm is high accuracy resulted and less time complexity in 98.97% cataloguing accuracy.