Machine and Deep Learning Algorithms for Classifying Different Types of Dementia: A Literature Review Publisher



Noroozi M¹ ; Gholami M² ; Sadeghsalehi H³ ; Behzadi S⁴ ; Habibzadeh A^{5, 6} ; Erabi G⁷ ; Sadatmadani SF⁸ ; Diyanati M⁹ ; Rezaee A¹⁰ ; Dianati M⁴ ; Rasoulian P¹¹ ; Khani Siyah Rood Y¹² ; Ilati F¹³ ; Hadavi SM¹⁴ Show All Authors Show Affiliations
Source: Applied Neuropsychology:Adult Published:2024

Abstract

The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer’s Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It’s important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study. © 2024 Taylor & Francis Group, LLC.