This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ech T Press Science Computers, Materials & Continua DOI: 10.32604/cmc.2023.032341 Article Novel Computer-Aided Diagnosis System for the Early Detection of Alzheimer’s Disease Meshal Alharbi and Shabana R. Ziyad* Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia *Corresponding Author: Shabana R. Ziyad. Email: s.ziyad@psau.edu.sa Received: 14 May 2022; Accepted: 15 September 2022 Abstract: Aging is a natural process that leads to debility, disease, and depen- dency. 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 empha- sizes the significance of pre-processing prior to classification to improve the classification results of the proposed CADx tool. Keywords: Alzheimer’s disease; dementia; mild cognitive impairment; computer-aided diagnosis; intelligent water drop algorithm; random forest 1 Introduction Dementia is a progressive neurological disorder resulting in the deterioration of memory capa- bility, cognitive skills, reasoning, understanding, judgment, emotions, personality, and behavior; in elderly patients, it affects their ability to perform daily activities [1]. Dementia is commonly diagnosed in the elderly, although it is not a common feature of the aging process. Dementia proceeds in three stages: early-stage, middle-stage, and late-stage. In early-stage dementia, patients show symptoms such as forgetfulness, losing track of time, and forgetting their way even in familiar places. Middle-stage dementia includes more severe symptoms such as repeating questions, forgetting recent events and the