Artificial Intelligence-Based Diabetes Diagnosis with Belief Functions Theory

Abstract

We compared various machine learning (ML) methods, such as the K-nearest neighbor (KNN), support vector machine (SVM), and decision tree and deep learning (DL) methods, like the recurrent neural network, convolutional neural network, long short-term memory (LSTM), and gated recurrent unit (GRU), to determine the ones with the highest precision. These algorithms learn from data and are subject to different imprecisions and uncertainties. The uncertainty arises from the bad reading of data and/or inaccurate sensor acquisition. We studied how these methods may be combined in a fusion classifier to improve their performance. The Dempster–Shafer method, which uses the formalism of belief functions characterized by asymmetry to model nonprecise and uncertain data, is used for classifier fusion. Diagnosis in the medical field is an important step for the early detection of diseases. In this study, the fusion classifiers were used to diagnose diabetes with the required accuracy. The results demonstrated that the fusion classifiers outperformed the individual classifiers as well as those obtained in the literature. The combined LSTM and GRU fusion classifiers achieved the highest accuracy rate of 98%.

Keywords

• artificial intelligence
• machine learning
• deep learning
• LSTM
• diabetes
• belief functions