Open AccessDiagnosis of Diabetes Mellitus in Women of Reproductive Age using the Prediction Methods of Naive Bayes, Discriminant Analysis, and Logistic Regression
Author(s) -
Yulia Resti,
Endang Sri Kresnawati,
Novi Rustiana Dewi,
Des Alwine Zayanti,
Ning Eliyati
Publication year - 2021
Publication title -
science and technology indonesia
Language(s) - English
Resource type - Journals
eISSN - 2580-4391
pISSN - 2580-4405
DOI - 10.26554/sti.2021.6.2.96-104
Subject(s) - logistic regression , multinomial logistic regression , linear discriminant analysis , diabetes mellitus , bayes' theorem , naive bayes classifier , medicine , statistics , artificial intelligence , computer science , mathematics , bayesian probability , endocrinology , support vector machine
Diabetes is a chronic disease that can cause serious illness. Women are four times more likely to develop heart problems caused by diabetes. Women are also more prone to experience complications due to diabetes, such as kidney problems, depression, and decreased vision quality. Nearly 200 million women worldwide are affected by diabetes, with two out of five affected by the disease being women of reproductive age. This paper aims to predict women with at least 21 years of age having diabetes based on eight diagnostic measurements using the statistical learning methods; Multinomial Naive Bayes, Fisher Discriminant Analysis, and Logistic Regression. Model validation is built based on dividing the data into training data and test data based on 5-fold cross-validation. The model validation performance shows that the Gaussian Naïve Bayes is the best method in predicting diabetes diagnosis. This paper’s contribution is that all performance measures of the Multinomial Naïve Bayes method have a value greater than 93 %. These results are beneficial in predicting diabetes status with the same explanatory variables.