Classification of Moderate and Advanced Alzheimer's Patients Using Radial Basis Function Based Neural Networks Initialized with Fuzzy Logic

Abstract

Background: Alzheimer's disease can be diagnosed through various clinical methods. Among them, electroencephalography has proven to be a powerful, non-invasive, affordable, and painless tool for its diagnosis. Objectives: In this study, eight machine learning (ML) approaches, including SVM, BLDA, DT, GNB, KNN, RF, and deep learning (DL) methods such as RNN and RBF, were employed to classify Alzheimer's disease into two stages: moderate Alzheimer's disease (ADM) and advanced Alzheimer's disease (ADA). Material and methods: To this aim, electroencephalography data collected from five different hospitals over a decade has been used. A novel method based on neural networks has been proposed to increase accuracy and obtain fast classification times. Results: Results show that deep neuronal networks based on radial basis functions initialized with fuzzy means achieved the best balanced accuracy with 96.66% accuracy in ADA classification and 93.31% accuracy in ADM classification. Conclusion: Apart from improving accuracy, it is noteworthy that this algorithm had never been used before to classify patients with Alzheimer's disease.