## 6 Conclusions  
This research explored the use of machine learning algorithms in the early detection of dementia, with a particular focus on the potential of cognitive features derived from a series of cognitive tests, and contrasted these with neuroimaging features in predictive model training. The unique contribution of this research lies in the implementation of the AdaBoost Ensemble model on cognitive features, yielding an enhanced accuracy rate of approximately 83%. The AdaBoost model demonstrated improved performance compared to other benchmark models, including the Artificial Neural Network, Support Vector Machine, and Naive Bayes. While the performance metrics improved when we combined cognitive and neuroimaging features, we emphasized cognitive features because of their clinical convenience and ease of execution. Furthermore, our work underscores the significance of cognitive assessments in the early detection of dementia, suggesting that clinicians should prioritize evaluating specific cognitive elements during AD screening. By shedding light on which cognitive areas are pivotal, this research informs clinicians about optimal times for cognitive feature assessment and how it complements the biomarker assessments in the diagnosis trajectory. Future work should consider refining these machine-learning models, exploring other machine-learning algorithms, and enhancing performance using cognitive features alone. Future work should also consider analyzing different datasets or patient populations to validate the general applicability of the model. Overall, this research paves the way for innovative, machine-learning-assisted strategies for early dementia detection, promoting the use of easily accessible cognitive tests. Conceptualization, M.E. ; Methodology, S.S. and M.E. ; Software (Python 3.0), M.I. ; Validation, S.S. and M.E. ; Investigation, M.I. ; Resources, S.S. and M.E. ; Writing--original draft, M.I., S.S. and M.E. ; Writing--review & editing, M.I. All authors have read and agreed to the published version of the manuscript. We gratefully acknowledge MDPI for generously granting us an APC waiver valued at 2070 CHF, thereby facilitating the publication of this research. This kind gesture significantly supports our efforts to disseminate our findings to the wider scientific community. Not applicable. Not primary data were collected. The data used in this study were retrieved from the Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) dataset [14]. Access to the ADNI-related datasets is contingent on adherence to the ADNI Data Use Agreement. The authors declare that they have not used Artificial Intelligence (AI) tools in the writing of this article. However, ChatGPT 4 was employed for coprediting and proofreading certain sections, including the abstract, introduction, and conclusion. The authors took precautions to ensure that ChatGPT 4 did not introduce any text that was not authored by the original writers. Throughout the process, the authors reviewed and revised the suggestions provided by ChatGPT 4 to maintain the accuracy and integrity of the final manuscript. Moreover, the authors would like to express their sincere gratitude to the Alzheimer's Disease Neuroimaging Initiative (ADNI) for providing access to the TADPOLE challenge dataset. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research and Development, LLC. ; Johnson and Johnson Pharmaceutical Research and Development LLC. ; Lumosity; Lundbeck; Merck and Co., Inc.; Meso Scale Diagnostics, LLC.