Chronic kidney disease (CKD) is a global public health problem with an increased prevalence and incidence of kidney failure, poor prognosis and high costs. prevalence values throughout Indonesia for kidney failure have an average value of around 0.2 percent. The first step in managing kidney disease is determining the right diagnosis. Then we need a method to predict chronic kidney disease. Naïve Bayes has several advantages, which are fast in calculations, simple and high accuracy algorithms. The Naïve Bayes Classifier is more appropriately applied to large data and can handle incomplete data (missing value) and is strong against irrelevant attributes and noise in the data. To improve accuracy, Particle Swarm Optimization is used for weighting attributes. From the results of the Naive Bayes Classification research based on Particle Swarm Optimization, the accuracy of confusion matrix is 98.75% and AUC 99% . while Naive Bayes has an accuracy of 97.00% confusion matrix and AUC 99.8%.
Prediction of kidney disease, Naive bayes, Particle Swarm Optimization.
|Penulis||: Toni Arifin, Daniel Ariesta|
|Tanggal Terbit||: 30 April 2019|
|Kategori||: Jurnal Terakreditasi|
|Penerbit||: LLDIKTI 4|
|Reputasi||: SINTA 3|