Integrating Cognitive and Neuroimaging Data to Enhance Alzheimer's Disease Diagnosis

Effective therapy for Alzheimer's disease depends on early diagnosis. Key results from four research that used machine learning to identify Alzheimer's disease in its initial level. Early research indicates that combining cognitive and neuropsychological data may enhance diagnosis. The second research interprets neuroimaging data using machine learning methods. Finding the first signs of illness is the objective here. In the third research, risk variables for Alzheimer's disease are found using machine learning algorithms. They stress the need of comprehensive data for precise prediction. A novel algorithm is presented in the fourth research. The soft voting classifier method increases recognition accuracy by operating on an averaging mechanism. These papers demonstrate how machine learning may enhance approaches for diagnosing Alzheimer's disease. To enhance therapeutic results, combine everything from cognitive evaluations to multimodal data.