A Review on Alzheimer's Disease Detection using Different Approaches

Computer Engineering

Millions of individuals worldwide suffer from Alzheimer's disease (AD), a progressive neurological ailment marked by memory loss and cognitive impairment. Effective treatment and management depend on early discovery, yet conventional diagnostic techniques, such clinical evaluations and cognitive testing, frequently fail to recognize the disease in its early stages. By analyzing complex datasets, including as neuroimaging, genetic data, and other biomarkers, machine learning (ML) has transformed the area of medical diagnostics and opened up new paths for the early and accurate identification of AD. This review paper offers an extensive summary of the many machine learning (ML) methods, including as supervised learning, unsupervised learning, and deep learning approaches, that are employed in the identification of AD. It draws attention to noteworthy related research that effectively apply these techniques to various kinds of data, indicating how efficient they are at enhancing diagnostic precision. The study also examines the benefits and drawbacks of each strategy, offering insights into the difficulties and potential paths for ML's use in AD detection in the future. This study intends to guide future research and promote the creation of more reliable, understandable, and integrated machine learning models for the early detection of Alzheimer's disease by examining these various approaches.

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