Advancing Early Detection of Alzheimer's Disease: A Comprehensive Review of Machine Learning Approaches

Early detection of Alzheimer’s disease is essential for effective treatment. Four studies using machine learning to detect early Alzheimer’s disease show key findings. Early studies suggest that integrating neuropsychological and cognitive data may improve diagnosis. The second study uses machine learning algorithms to interpret neuroimaging data. The goal here is to detect the onset of symptoms. The third study uses machine learning models to uncover risk factors for Alzheimer’s disease. They emphasize the importance of complete data for predictive accuracy. The fourth study presents a new algorithm. The soft voting classifier algorithm works based on the averaging mechanism to improve the recognition accuracy. These studies show how machine learning can improve Alzheimer’s disease diagnosis methods. Combine everything from cognitive assessments to multimodal data to improve clinical outcomes. This leads to better care for people with dementia.