Prediction Model of Type 2 Diabetes Mellitus for Oman Prediabetes Patients Using Artificial Neural Network and Six Machine Learning Classifiers

Abstract

The early diagnosis of type 2 diabetes mellitus (T2DM) will provide an early treatment intervention to control disease progression and minimise premature death. This paper presents artificial intelligence and machine learning prediction models for diagnosing T2DM in the Omani population more accurately and with less processing time using a specially created dataset. Six machine learning algorithms: K-nearest neighbours (K-NN), support vector machine (SVM), naive Bayes (NB), decision tree, random forest (RF), linear discriminant analysis (LDA), and artificial neural networks (ANN) were applied in MATLAB. All data used were clinical data collected manually from a prediabetes register and the Al Shifa health system of South Al Batinah Province in Oman. The results were compared with the most widely used Pima Indian Diabetes dataset. Eleven clinical features were taken into consideration for predicting T2DM. The random forest and decision tree models performed better than all the other algorithms, providing an accuracy of 98.38% for Oman data. When the same model and number of features were used, the accuracy obtained with the Oman dataset exceeded PID by 9.1%. The analysis showed that T2DM diagnosis efficiency increased with more features, which is of help in the case of many missing values.