Effective Use of Naïve Bayes, Decision Tree, and Random Forest Techniques for Analysis of Chronic Kidney Disease

摘要: The researcher is using a classification method for chronic kidney patient analysis of data. Chronic disease data contains 25 attributes and 400 instances. Now, we proposed best model by applying the decision support system naive Bayes, tree J48 algorithm, random forest classifier techniques, really, this will be helpful to predict further CKD as well not patients on basis different parameters. During analysis, Bayes correctly classifies instances with 97.50% accuracy, algorithm finds classified 98.33% similarly, analyzed giving output 100% accuracy 0% incorrectly Therefore, produces most accurate correct results 70 percent split value (train portion test remainder), ROC area 1%. main objective research paper comparative study NB classifier, DT J48, RF analyze (CKD) patient’s how many are having CKD. An currently have people may in future has been attributed it. When analyzing same shows that variant can diagnosed future, it 100%.