An Optimized Decision Tree with Genetic Algorithm Rule-Based Approach to Reveal the Brain's Changes During Alzheimer's Disease Dementia

Background: It is desirable to achieve acceptable accuracy for computer aided diagnosis system (CADS) to disclose the dementia-related consequences on the brain. Therefore, assessing and measuring these impacts is fundamental in the diagnosis of dementia. Objective: This study introduces a new CADS for deep learning of magnetic resonance image (MRI) data to identify changes in the brain during Alzheimer's disease (AD) dementia. Methods: The proposed algorithm employed a decision tree with genetic algorithm rule-based optimization to classify input data which were extracted from MRI. This pipeline is applied to the healthy and AD subjects of the Open Access Series of Imaging Studies (OASIS). Results: Final evaluation of the CADS and its comparison with other systems supported the potential of the proposed model as a novel tool for investigating the progression of AD and its great ability as an innovative computerized help to facilitate the decision-making procedure for the diagnosis of AD. Conclusion: The one-second time response, together with the identified high accurate performance, suggests that this system could be useful in future cognitive and computational neuroscience studies.