Abstract
Dementia is a broad term for a large number of conditions, and it is often associated with Alzheimer’s disease. A reliable diagnosis of this disease, especially in the early stages, may prevent further complications. As such, machine learning algorithms can be applied in order to validate and correctly classify cases of dementia or non dementia in adults, assisting physicians in the diagnosis and management of this clinical condition. In this study, a dataset containing magnetic resonance imaging comparisons of demented/non demented adults was used to conduct a Data Mining process, following the Cross Industry Standard Process for Data Mining methodology, with the main goal of classifying instances of dementia. Different machine learning algorithms were applied during this process, more specifically Support Vector Machines, Decision Trees, Logistic Regression, Neural Networks, Naïve Bayes and Random Forest. The maximum accuracy of 95.41% was achieved with the Naïve Bayes algorithm using Split Validation.
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Acknowledgements
This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
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Neto, C. et al. (2022). Classification of Dementia in Adults. In: Rocha, Á., Fajardo-Toro, C.H., Rodríguez, J.M.R. (eds) Developments and Advances in Defense and Security . Smart Innovation, Systems and Technologies, vol 255. Springer, Singapore. https://doi.org/10.1007/978-981-16-4884-7_23
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DOI: https://doi.org/10.1007/978-981-16-4884-7_23
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