Abstract
Aging is a natural process that leads to debility, disease, and dependency. Alzheimer's disease (AD) causes degeneration of the brain cells leading to cognitive decline and memory loss, as well as dependence on others to fulfill basic daily needs. AD is the major cause of dementia. Computer-aided diagnosis (CADx) tools aid medical practitioners in accurately identifying diseases such as AD in patients. This study aimed to develop a CADx tool for the early detection of AD using the Intelligent Water Drop (IWD) algorithm and the Random Forest (RF) classifier. The IWD algorithm an efficient feature selection method, was used to identify the most deterministic features of AD in the dataset. RF is an ensemble method that leverages multiple weak learners to classify a patient's disease as either demented (DN) or cognitively normal (CN). The proposed tool also classifies patients as mild cognitive impairment (MCI) or CN. The dataset on which the performance of the proposed CADx was evaluated was sourced from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The RF ensemble method achieves 100% accuracy in identifying DN patients from CN patients. The classification accuracy for classifying patients as MCI or CN is 92%. This study emphasizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool.