Open AccessOptimization the Naive Bayes Classifier Method to diagnose diabetes Mellitus
Author(s) -
Desi Susilawati,
Dwiza Riana
Publication year - 2021
Publication title -
iaic transactions on sustainable digital innovation
Language(s) - English
Resource type - Journals
ISSN - 2715-0461
DOI - 10.34306/itsdi.v1i1.21
Subject(s) - naive bayes classifier , diabetes mellitus , particle swarm optimization , classifier (uml) , artificial intelligence , bayes' theorem , bayes classifier , type 2 diabetes mellitus , medicine , machine learning , pattern recognition (psychology) , computer science , mathematics , bayesian probability , support vector machine , endocrinology
World Health Organization (WHO) states that Diabetes Mellitus is the world's top deadly disease. several studies in the health sector including diabetes mellitus have been carried out to detect diseases early. In this study optimization of naive bayes classifier using particle swarm optimization was applied to the data of patients with 2 classes namely positive diabetes mellitus and negative diabetes mellitus and data on patients with 3 classes, those who tested positive for diabetes mellitus type 1, diabetes mellitus type 2 and negative diabetes mellitus. After testing, the algorithm of Naive Bayes Classifier and Naive Bayes Classifier based on Particle Swarm Optimization, the results obtained are the Naive Bayes Classifier method for 2 classes and 3 classes each producing an accuracy value of 78.88% and 68.50%. but after adding Particle Swarm Optimization the value of accuracy increased respectively to 82.58% and 71, 29%. The classification results for 2 classes have an accuracy value higher than 3 classes with a difference of 11.29%