###### Abstract  
Dementia, including Alzheimer's Disease (AD), is a complex condition, and early detection remains a formidable challenge due to limited patient records and uncertainty in identifying relevant features. This paper proposes a machine learning approach to address this issue, utilizing cognitive and neuroimaging features for training predictive models. This study highlighted the viability of cognitive test scores in dementia detection--a procedure that offers the advantage of simplicity. The AdaBoost Ensemble model, trained on cognitive features, displayed a robust performance with an accuracy rate of approximately 83%. Notably, this model surpassed benchmark models such as the Artificial Neural Network, Support Vector Machine, and Naive Bayes. This study underscores the potential of cognitive tests and machine learning for early dementia detection.  
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used to examine brain activities and diagnose dementia [8]. However, standard, state-of-the-art diagnostic procedures for dementia, such as cerebrospinal fluid analysis tests and neuroimaging tests, are often expensive, time-consuming, and carry risk factors [9].  
Therefore, there is growing interest in using Machine Learning (ML) technologies to predict and detect the early stages of dementia. For example, the work reported in [10] proposed using a Machine Learning algorithm to detect the stages of dementia using screening tests. [PERSON] et al. [11] detailed how machine learning techniques, including the SVM (Support Vector Machines) and KNN (K-Nearest Neighbor) algorithms, are used for predicting and classifying dementia. [PERSON] et al. [12] leveraged machine learning to develop a decision-making CAD (Computer-Aided Design) tool for detecting dementia.  
In response to these developments, this paper proposes the use of Machine Learning (ML) for the early-stage detection of Alzheimer's Disease. The paper is an extended version of the paper \"Early Detection of Alzheimer's Disease: A Novel Cognitive Feature Selection Approach Using Machine Learning\" published in the proceedings of the 2021 Conference on Advances in Information, Communication and Cybersecurity [13]. Our research blends applied ML methods with a novel feature selection technique. Key objectives include:  
* Utilizing all available features (cognitive, neuroimaging, and combined) from the ADNI-1, ADNI-2, and ADNI-3 datasets for Alzheimer's Disease detection
* Implementing robust preprocessing techniques, including handling missing values and data normalization.
* Proposing and employing the novel NCA-F feature selection method to pinpoint critical and relevant features.  
Conducting comparative analyses using various machine learning methods on the selected features building on these objectives, the model proposed in this article implements the AdaBoost Ensemble (adB), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Naive Bayes (NB) machine learning algorithms with cognitive and neuroimaging features obtained from a public dataset [14] consisting of 13,916 patients' records. The remainder of this paper is organized as follows: Section 2 discusses the role of machine learning in predicting dementia; Section 3 outlines the research methodology; and Section 4 analyzes the results of the implemented machine learning models and compares them with existing work. Section 6 details the limitations of this work, while Section 5 offers concluding remarks.