Machine and deep learning algorithms for classifying different types of dementia: A literature review

Area of Science:

• Neurology
• Computer Science
• Medical Informatics

Background:

• Dementia, a global cognitive impairment, affects millions. Early identification and treatment are crucial for managing conditions like Alzheimer's, frontotemporal, Lewy body, and vascular dementia.
• Machine learning (ML) and deep learning (DL) offer potential advancements in diagnosing and managing dementia.

Purpose of the Study:

• To review the literature on the application of ML algorithms for diagnosing various types of dementia.
• To compare different ML algorithms, discuss feature selection, data preparation, and their role in predicting disease progression and treatment response.

Main Methods:

• Literature review of ML algorithms applied to dementia diagnosis.
• Comparison of algorithms including support vector machines, artificial neural networks, decision trees, and random forests.
• Discussion on feature selection, data preparation, and predictive capabilities of ML models.

Main Results:

• ML algorithms demonstrate potential for improving the accuracy of dementia diagnosis, particularly in early stages.
• Different ML algorithms have varying benefits and drawbacks for dementia diagnosis.
• Careful feature selection and data preparation are key to developing accurate ML models.

Conclusions:

• ML algorithms can assist in the early diagnosis and prediction of dementia progression and treatment response.
• Further research is needed to validate ML and DL efficacy in clinical settings and address ethical concerns.
• ML tools should augment, not replace, clinical judgment for definitive dementia diagnosis.