Alzheimer’s Disease Diagnosis Using Machine Learning and Deep Learning Techniques

A major global problem is dementia, a disorder that causes the loss of cognitive abilities. Effective therapy and management depend on early detection. By examining multiple sorts of data, including brain scans, speech, and gait, deep learning (DL) and machine learning (ML) algorithms have shown promising results in detecting dementia. Using DL and ML approaches, this survey study thoroughly summarizes recent improvements and discoveries in the dementia diagnosis sector. The most recent methods for dementia detection, including supervised, unsupervised, and reinforcement learning approaches, are reviewed in this work. This paper's main goal is to explore various traditional machine learning approaches widely applied for identifying and forecasting Alzheimer's disease (AD) using MRI and linguistic datasets. The merits and demerits of the various data formats employed in DL and ML models for dementia detection are also covered. There is also a discussion of the difficulties of applying DL and ML methods for dementia detection, such as data imbalance, interpretability, and generalization. Using linguistic data, including speech and text, has become increasingly popular lately to aid in detecting dementia. An exhaustive review of the existing research on various linguistic markers used to diagnose dementia is provided. The paper concludes by discussing the future directions of DL and ML techniques in dementia detection and their potential impact on early detection and treatment. With treatment, early detection of dementia can enhance the patient's quality of life, and DL/ML techniques can aid in identifying the disease. The paper aims to provide researchers and practitioners in dementia detection with a comprehensive comprehension of the present cutting-edge problems and opportunities connected with DL and ML approaches.