A Comparative Survey of Machine Learning Approaches for Alzheimer’s Disease Prediction and Detection

Alzheimer's disease (AD) is a neurological illness that affects a large proportion of the senior population. Early detection and prognosis are crucial for improving patient care and developing effective treatment strategies. Machine learning methods, such as voice analysis, neuroimaging, and clinical data, have showed promise in detecting and predicting Alzheimer's disease. This study presents at thorough assessment and comparative analysis of 15 research papers, highlighting their contributions to the area. Deep learning models like convolutional neural networks and recurrent neural networks have demonstrated great accuracy in differentiating between cognitively normal, dementia, and Alzheimer's dementia. Incorporating hybrid data sources, developing interpretive approaches, and researching disease-specific traits and mechanisms are all possible future paths. Overall, machine learning approaches have the potentialto increase the accuracy and efficiency of Alzheimer's disease detection, resulting in earlier intervention and better patient outcomes.