Classification of Diabetes Mellitus (DM) using Machine Learning Algorithms - Sirajun Noor, Noor Azmiya (Universiti Teknologi PETRONAS, 2021)
Diabetes Mellitus (DM) is one of the most prevalent disease in the world today which is associated by having high glucose level in body either due to inadequate production of insulin or the body cell’s not responding towards the produced insulin. Data mining and machine learning techniques can be extremely useful in classification of DM considering the need to have a shift from current traditional method which uses sharp needles to draw blood towards a non – invasive method. The objective of this study is to perform DM classification using various machine learning algorithms using Weka as a tool. In this paper, single classifiers such as Support Vector Machine, Naïve Bayes, Bayes Net, Decision Stump, k – Nearest Neighbors, Logistic Regression, Multilayer Perceptron and Decision Tree is experimented. Apart from that, ensemble methods such as bagging, adaptive boosting using AdaBoostM1, hybrid classifier using combinations of Random Forest with various base classifiers and ensemble algorithm which is the Random Forest has also been studied. In this research, it was found that performance of ensemble method using hybrid classifier of Random Forest – Bayes Net model was found as the best DM classification model with an accuracy of 83.91% using the Pima Indian Diabetes Dataset (PIDD) out beating all the other classification algorithms. Whereas for the German Frankfurt dataset, best DM classification model was found using Random Forest algorithm with an accuracy of 98.77%.